A. Introduction

Monetary policy frameworks of the ASEAN-5 economies have on the whole performed well since the AFC, delivering both price and financial stability. The flexible inflation targeting frameworks put in place post-AFC alongside the move to greater exchange rate flexibility has served the ASEAN-5 economies well and provides lessons to other EMDEs. The region was also relatively resilient to the GFC as a result of a decade of financial and structural reforms following the AFC with refinements to the monetary policy framework playing an important role. However, the generalized reduction in global interest rates and loose liquidity conditions during the great moderation and UMP period pose a challenge to the traditional “trilemma” view as flexible exchange rates could not fully insulate economies from the global financial cycle, when the capital account is highly open.

The ASEAN-5 central banks were therefore compelled to adapt their policy framework and toolkits in order to strengthen policy autonomy and dampen risks. The policy toolkit has been broadened to MPPs to address systemic risks, and CFMs/FX intervention to manage volatile capital flows. The fallout, sources of resiliency and policy responses associated with capital outflow episodes provide valuable lessons for the current juncture where EMEs including the ASEAN-5 are facing the prospect of a prolonged period of capital outflows and risks of global financial volatility (IMF 2016a, b).

The cluster report complements the individual ASEAN-5 country reports by focusing on structural dimensions and past responses of monetary and exchange rate policies to common and idiosyncratic shocks, thereby giving context to the Article IV coverage of conjunctural policy settings. The report addresses three broad themes:

• It examines how monetary policy regimes have evolved since the AFC, to show how they have elected to accommodate the constraints imposed by the impossible trinity, highlighting similarities and differences across time and countries. While countries have generally moved toward greater exchange rate flexibility and capital account openness, they have also accumulated FX reserves to strengthen their external positions and smooth exchange rate fluctuations, while not targeting a specific level of the exchange rate

• The report then considers the channels through which global financial conditions have impacted domestic financial markets and monetary conditions. It assesses empirically the transmission of “center economy” monetary policy to domestic short- and long-term market interest rates, and retail bank rates. The results suggest the existence of a global financial cycle emanating from changes in U.S. monetary policy and global risk aversion that drives domestic financial conditions in the ASEAN-5 economies. However, policy rates and active liquidity management continued to be effective in influencing the retail bank rates and the yield curve.

• The third section of the report explores how monetary policy has responded to these challenges as well as the role of MPPs and other tools to manage volatile capital flows. To assess the former, we compare the behavior of the primary monetary policy instrument against forecasts based on country-specific estimated Taylor rule reaction functions including the weight placed on policy goals other than inflation. The ASEAN-5 economies also increased their reliance on MPPs to address systemic risks, particularly sectoral leverage and asset price cycles. CFMs and FX intervention were used as part of the toolkit to manage volatile capital flows in line with the Fund’s institutional view with a greater reliance on exchange rate flexibility to cushion against capital flow shocks. A concluding section discusses the lessons from the ASEAN-5 experience.

B. ASEAN-5 Monetary Policy Frameworks

1. The monetary policy framework encompasses the institutional structure of the central bank as well as the specification of its goals, instruments, strategy, operating targets and procedures, and communications (IMF 2015a). The institutional setup includes the central bank’s statutory mandate, governance structure, and decision-making processes. The monetary policy strategy guides the setting of the central bank’s operating targets, and its operating procedures, and specify how its policy instruments should be adjusted to implement those targets. Central bank communication aids the public in understanding the policy framework as well as the rationale for specific policy decisions and helps shape market expectations. It promotes transparency and accountability of the central bank. A general consensus has emerged on the set of principles that characterize effective policy frameworks in countries with scope for independent monetary policy (IMF 2015a).

2. ASEAN-5 monetary policy frameworks have evolved to embody the key characteristics of a coherent forward-looking monetary policy framework (Appendix I). In particular, Indonesia, Philippines and Thailand adopted an inflation targeting (IT) framework while Singapore developed a more rigorous implicit IT regime. Bank Negara Malaysia adopted a fixed exchange rate regime in the aftermath of the AFC but in 2005, it moved to a flexible exchange rate regime and a monetary policy framework that focus on price stability but also takes into consideration on the impact of monetary policy on financial stabilty. While the frameworks differ in terms of their exact characteristics, especially with respect to instruments, operating targets, and intermediate targets, all of the ASEAN-5 central banks generally have a clear statement of internally consistent goals of policy, the institutional arrangements that give the central bank the freedom to pursue these goals, and transparency and effective communication with respect to its goals and policy actions (see Appendix I). Price stability is the primary objective of monetary policy over the policy horizon for all ASEAN-5 central banks although many of them are also required to consider output and employment conditions as in other AEs and EMEs.² The clear independent operation frameworks also enhance the central bank’s accountability for fulfilling its objectives that are well communicated to the general public and market participants through regular reports, press conferences, and dialogue. Even in the somewhat special cases of Malaysia and Singapore where the inflation and intermediate targets, respectively are not explicitly disclosed, the policy actions and intentions are well articulated to the market so that market participants have a good idea of what the central banks’ tolerance levels are for inflation. The central bank transparency scores for the ASEAN-5 are comparable to other IT EMEs reflecting the strong communication and transparency practices of the ASEAN-5 central banks (Figure 1).

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Degree of Central Bank Transparency ^1/

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Source: Dincer and Eichengreen (2014).^1/The de jure transparency index was developed by Dincer and Eichengreen (2014). It ranges from 0–15, and is the sum of scores to questions ranging from political, economic, procedural, policy and operational transparency. Median value of transparency scores were used for country groupings.

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3. The ASEAN-5 monetary policy frameworks have delivered a strong inflation performance similar to other IT emerging market and developing economies (EMDEs). Most EMDEs have achieved lower inflation amidst marginal declines in growth between the periods 1991–2000 and 2001–2014. However, countries that adopted IT regimes have reduced inflation and volatility more than their non-IT counterparts (IMF 2015a and Roger 2010). The ASEAN-5 economies have also reduced output and inflation volatility, reaching levels achieved by IT economies after adopting IT regimes (Figure 2). Looking more closely, the ASEAN-5 IT countries (Indonesia, Philippines and Thailand) have performed even better with higher GDP growth and lower inflation as well as lower GDP growth and inflation volatility, probably reflecting greater scope for catch up and stabilization as well as other potential factors at play.

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ASEAN-5: Growth and Inflation ^1/–^3/

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

^1/Following Roger (2010), hollow symbols represent periods from 1991 to 2000 or up to year of IT adoption. Filled in symbols represent periods from 2001 or a year after IT adoption to 2014. The straight lines represent the direction of movement between the two periods.2/ Median value of country averages were used for real GDP growth; median of median values for inflation.^3/Median standard deviation for growth and inflation were used for volatility.

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C. Impossible Trinity

4. Greater exchange rate flexibility has bestowed monetary policy autonomy. To present the evolution of the policy choices of the ASEAN-5, monetary trilemma triangles are calibrated for each country following Aizenmann, Chinn and Ito (2012), with some adjustments.³ We focus on three non-crisis periods 1990–96, 2000–07, and 2010–14 to avoid outliers. Comparing the post-GFC period (2010–14) with the pre-AFC period (1990–96), all ASEAN-5 economies have moved toward greater monetary policy autonomy, generally by forgoing exchange rate stability (Figure 3). However, the transition from the pre-AFC to the post-GFC regimes has been different across countries:

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ASEAN-5: Trilemma Triangles

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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• Before the AFC, Indonesia had a crawling peg exchange rate system and an open capital account, which limited its ability to set interest rates. After the AFC, Indonesia adopted a more flexible exchange rate regime, which allowed it greater independence in setting its interest rate. Since the GFC, Indonesia increased its exchange rate flexibility and introduced CFM measures, providing further autonomy to set interest rates.

• Before the AFC, Malaysia had a managed exchange rate and an open capital account, which provided limited scope to set domestic interest rates. After the AFC, Malaysia fixed the exchange rate and managed the capital account in order to be able to gain some monetary independence. Malaysia de-pegged its exchange rate in 2005 and adopted a more flexible exchange rate regime and liberalized its capital account, which provided greater autonomy to set interest rates during and after the GFC.

• Prior to the AFC, the Philippines had a relatively closed capital account and a managed exchange rate regime, which allowed for a fair degree of monetary policy independence. After the AFC, the Philippines gradually liberalized its capital account restrictions and continued to manage its exchange rate to build up FX reserves, reducing its independence in setting interest rates. Since the GFC, the Philippines has had a more flexible exchange rate regime, which has increased its independence in setting interest rates.

• Singapore position in the monetary policy trilemma has remained relatively unchanged. As a financial center, Singapore has a highly open capital account. It also has a unique monetary policy regime centered on the management of the exchange rate. As a result, it has limited control over the setting of interest rates, which are market determined.

• Before the AFC, Thailand had a managed exchange rate regime and an open capital account, which provided limited scope for setting interest rates. After the AFC, Thailand adopted a more flexible exchange rate regime and managed its capital account more tightly, which provided for some interest rate autonomy. Since the GFC, Thailand has allowed even more exchange rate flexibility and gained more interest rate autonomy.

5. The move towards greater exchange rate flexibility has supported the transition to a more consistent forward-looking monetary policy framework as in other EMEs (IMF 2015b). The Annual Report on Exchange Arrangements and Exchange Restriction (AREAER) shows a similar transition of the monetary policy and exchange rate frameworks in the ASEAN-5 countries since the early 2000s as in the trilemma triangles above. According to the AREAER classification, the ASEAN-5 economies have moved toward greater exchange rate flexibility, with all five of them classified as de jure managed or free floaters since 2008 (Figure 4).⁴ However, this move has been less pronounced in the de facto classification, with four economies classified as managed floaters in 2014 and none classified as free floaters. This is not inconsistent with the experience of many AEs and EMEs that have successfully adopted IT regimes, where the move towards a floating exchange rate regime was gradual and exchange rate considerations continue(d) to play a role in the conduct of monetary policy especially during crisis periods (IMF 2015b). In fact, the number of IT EMDEs classified as de facto managed floaters has risen through time, albeit with fewer countries classified in the intermediate category. That said, the lower de facto exchange rate flexibility in the ASEAN-5 economies compared to other EMEs does warrant a closer examination to identify and understand the role of the exchange rate in the evolving monetary policy frameworks.

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De Jure and De Facto Exchange Rate Classifications

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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D. Exchange Rate Behavior in ASEAN-5

6. There does not appear to be a consistent pattern among the ASEAN-5 exchange rates. One can notice broad co-movements among various subsets of the ASEAN-5 currencies over certain periods, which is not surprising given that they are neighbors, trading partners and competitors. At the same time, the magnitudes of exchange rate changes and the turning points differed across countries, and the groups of currencies moving together sometimes differed across periods. Thus, there is little evidence that the ASEAN-5 currencies or a subset thereof are bound together in a tight “club” or peg to a reserve currency or basket of currencies. To more formally assess this, we test for unit roots in the exchange rates of the ASEAN-5 currencies against the U.S. dollar, the yen, and the renminbi, as well as against each another (Klyuev and Dao, forthcoming).

7. The degree of exchange rate fixity declined over time. Though a low power test, the hypothesis of no unit root is rejected at 5 percent significance level for a number of ASEAN-5 countries against the U.S. dollar for the pre-AFC period. This confirms the narrative of quasi-dollar-pegs in Southeast Asia before the AFC that may have contributed to the buildup in external vulnerabilities (Jeasakul and others 2014). Between the AFC and the GFC, the unit root test only picks up the ringgit quasi peg to the U.S. dollar until 2005. Finally, after the GFC, the ASEAN-5 currencies remained non-stationary against the U.S. dollar, the yen, and the renminbi, indicating the absence of a tight relationship with the U.S. dollar or any other major currency. Cointegration tests between multiple currencies broadly confirm the unit root tests results and do not show any additional statistical relationships among the exchange rates.

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Exchange Rates Against U.S. Dollar

(2000:W1=100; increase = appreciation)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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8. ASEAN-5 central banks appear to smooth short-term currency volatility as stated in their FX management objectives, particularly against the U.S. dollar. The variability of the ASEAN-5 exchange rates against the U.S. dollar increases with the time horizon (see Table 1, and Figure 6). This is consistent with the notion that the authorities try to dampen day-to-day excessive exchange rate volatility⁵ but allow their currencies to move significantly over longer periods vis-à-vis one another and vis-à-vis any other major currency, including the U.S. dollar, the yen, and the renminbi (Klyuev and Dao, forthcoming).⁶ The time series analyses provide no statistically significant evidence of targeting a level of the nominal and/or real effective exchange rate as well as specific anchor currencies. Multiple regression analysis following Frankel and Wei (1994), show that ASEAN-5 currency movements against third currencies largely followed those of the U.S. dollar prior to the AFC. The Singapore dollar was more closely linked to a basket of currencies in which the U.S. dollar plays a dominant role, but the yen and the euro area had a significant weight. After the AFC, the Indonesian rupiah became considerably more volatile, while at the opposite end of the spectrum the Malaysian ringgit was pegged to the U.S. dollar until 2005. Thailand’s baht appears to have shifted some weight to the yen, with the dollar still by far the most significant anchor, and to have increased slightly the degree of flexibility. The peso was broadly on an appreciating trend against a combination of the U.S. dollar and the yen post-AFC, but exhibited greater volatility through time. After the ringgit peg with the U.S. dollar was broken, the ringgit moved more freely. Perhaps surprisingly, only a slight increase in the ASEAN-5 exchange rate volatility against the U.S. dollar can be observed in the years following the GFC compared to the pre-GFC period. This suggests that the ASEAN-5 central banks might have sought to counter the increasingly volatile environment associated with unconventional monetary policies in advanced economies with an increasing amount of FX intervention.

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Coefficient of Variation of Exchange Rates Against U.S. Dollar at Different Horizons

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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Table 1.

Exchange Rate Volatility—Coefficient of Variation ^1/

^1/Time periods: Pre-AFC (1991-June 1997); GFC (September 2008-February 2009); and Post-GFC (March 2009 to latest data).

Table 1.

Exchange Rate Volatility—Coefficient of Variation ^1/

		10-day			50-day			250-day
		Pre-AFC	GFC	Post-GFC	Pre-AFC	GFC	Post-GFC	Pre-AFC	GFC	Post-GFC
ASEAN-5
	Indonesia	0.10	1.33	0.50	0.31	4.17	1.27	1.14	5.78	4.04
	Malaysia	0.23	0.62	0.54	0.61	1.77	1.25	1.76	3.79	2.89
	Philippines	0.24	0.75	0.39	0.78	1.87	0.88	3.09	5.60	1.91
	Singapore	0.24	0.73	0.39	0.60	1.90	0.90	1.69	3.28	2.23
	Thailand	0.18	0.43	0.33	0.40	1.01	0.87	0.79	4.34	2.15
Other Asian free-floaters
	Australia	0.51	2.45	0.85	1.14	5.91	1.97	2.55	11.69	5.43
	New Zealand	0.42	2.20	0.92	0.95	5.09	2.08	2.44	10.76	5.07
	Japan	0.71	1.28	0.67	1.73	2.75	1.51	4.55	4.63	4.08

^1/Time periods: Pre-AFC (1991-June 1997); GFC (September 2008-February 2009); and Post-GFC (March 2009 to latest data).

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Table 1.

Exchange Rate Volatility—Coefficient of Variation ^1/

		10-day			50-day			250-day
		Pre-AFC	GFC	Post-GFC	Pre-AFC	GFC	Post-GFC	Pre-AFC	GFC	Post-GFC
ASEAN-5
	Indonesia	0.10	1.33	0.50	0.31	4.17	1.27	1.14	5.78	4.04
	Malaysia	0.23	0.62	0.54	0.61	1.77	1.25	1.76	3.79	2.89
	Philippines	0.24	0.75	0.39	0.78	1.87	0.88	3.09	5.60	1.91
	Singapore	0.24	0.73	0.39	0.60	1.90	0.90	1.69	3.28	2.23
	Thailand	0.18	0.43	0.33	0.40	1.01	0.87	0.79	4.34	2.15
Other Asian free-floaters
	Australia	0.51	2.45	0.85	1.14	5.91	1.97	2.55	11.69	5.43
	New Zealand	0.42	2.20	0.92	0.95	5.09	2.08	2.44	10.76	5.07
	Japan	0.71	1.28	0.67	1.73	2.75	1.51	4.55	4.63	4.08

^1/Time periods: Pre-AFC (1991-June 1997); GFC (September 2008-February 2009); and Post-GFC (March 2009 to latest data).

A. Global Financial Cycle and Domestic Financial Conditions

9. Global financial cycles and volatility spillovers pose a challenge for the ASEAN-5 countries. Eichengreen and Gupta (2014) argue that a key determinant of the severity of the impact of tapering talks is the volume of prior capital inflows. Rey (2013) argues that there is a global financial cycle in capital flows, asset prices, and credit growth, and that the cycle (proxied by VIX) is mainly driven by the U.S. monetary policy—affecting leverage of global banks, and cross-border capital/credit flows. Potential surprises from U.S. interest rate normalization and spikes in global risk aversion could be accompanied with capital outflows and tightening of domestic financial conditions that would have significant macrofinancial effects on the ASEAN-5 countries. Quantifying the impact and identifying the macrofinancial transmission channels are important to understand the role of monetary policy and potential for amplification of shocks.

10. Domestic financial conditions in the ASEAN-5 economies are sensitive to global factors. Following the approach of Miranda-Agrippino and Rey (2012), we estimate a principal component model to identify the underlying global factors that can explain the variability of a comprehensive set of domestic financial indicators.⁷ The principal component analysis shows that the first two common components explain about 60–75 percent of the variation of domestic financial conditions in the ASEAN-5 economies, with the exception of Singapore where the first principal component explains most of the variation. In general, in each economy, one of the first two principal components associated with the U.S. 10-year treasury bond are closely related to long term bond yields, retail bank interest rates, bank credit, and corporate sector indicators, while the other component associated with the VIX is correlated more closely with short term market rates, the exchange rate and stock market indicators (see Figure 7 and Table 2).⁸ More specifically, in the ASEAN-5 economies, there are two key macrofinancial transmission channels of global financial shocks: one related to the VIX and global financial cycle as in Blanchard and others (2015) that impact capital flows and asset prices; and another linked to U.S. interest rates that affects monetary and credit conditions.

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Co-movement of Latent Factors with Global Factors

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: IMF; CEIC Data Co., Ltd.; Haver Analytics; Bloomberg L.P.; and IMF staff estimates.1/ ***significant at p<0.10; **significant at p<.05; *significant at p<0.01.2/ The VIX coefficient for the Philippines refer to change in VIX rather than the VIX index, given the factor’s stronger association with the former.

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Table 2.

Cross Correlation of the Principal Factors and Global Variables ^{1/ 2/}

^1/***significant at p<0.10; **significant at p<.05; *significant at p<0.01.

^2/The VIX coefficient for the Philippines refer to change in VIX rather than the VIX index, given the factor’s stronger association with the former.

Table 2.

Cross Correlation of the Principal Factors and Global Variables ^{1/ 2/}

		U.S. 10-Year Government Bond Rate	VIX Index 2/
Indonesia	Factor 1	−0.741441 *	−0.576658 *
	Factor 2	0.014285	0.257853 **
Malaysia	Factor 1	−0.881109 *	−0.046
	Factor 2	0.181299 ***	−0.257 *
Philippines	Factor 1	−0.747989 *	−0.573 *
	Factor 2	−0.040962	0.57705 *
Singapore	Factor 1	−0.898969 *	0.223968 **
	Factor 2	0.095406	−0.694844 *
Thailand	Factor 1	−0.820087 *	−0.224075 **
	Factor 2	−0.151103	−0.334146 *

^1/***significant at p<0.10; **significant at p<.05; *significant at p<0.01.

^2/The VIX coefficient for the Philippines refer to change in VIX rather than the VIX index, given the factor’s stronger association with the former.

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Table 2.

Cross Correlation of the Principal Factors and Global Variables ^{1/ 2/}

		U.S. 10-Year Government Bond Rate	VIX Index 2/
Indonesia	Factor 1	−0.741441 *	−0.576658 *
	Factor 2	0.014285	0.257853 **
Malaysia	Factor 1	−0.881109 *	−0.046
	Factor 2	0.181299 ***	−0.257 *
Philippines	Factor 1	−0.747989 *	−0.573 *
	Factor 2	−0.040962	0.57705 *
Singapore	Factor 1	−0.898969 *	0.223968 **
	Factor 2	0.095406	−0.694844 *
Thailand	Factor 1	−0.820087 *	−0.224075 **
	Factor 2	−0.151103	−0.334146 *

^1/***significant at p<0.10; **significant at p<.05; *significant at p<0.01.

^2/The VIX coefficient for the Philippines refer to change in VIX rather than the VIX index, given the factor’s stronger association with the former.

B. Interest Rate Spillovers

11. While the role of global risk aversion on EMEs’ asset prices has been well studied, there is a need to take a closer look at spillovers on ASEAN-5’s domestic interest rates given their direct implications on the monetary policy framework. How the “center economy” monetary policies are transmitted to domestic long-term sovereign bond yields is of particular interest as they act as a benchmark for pricing corporate bonds and household mortgages. The influence of global financial factors and risk aversion on domestic retail bank rates, directly or indirectly, through the monetary transmission mechanism is also important given the dominance of banks in the ASEAN-5 economies.

• Domestic long-term market interest rates. The methodology followed Peiris (2013), estimating an EGARCH (1,1) model of sovereign bond yields in the ASEAN-5 economies during 2000–2015 using a comprehensive set of macrofinancial variables including global factors. The results show that a decline in the shadow federal funds rate⁹ reduces long-term government bond yields in all ASEAN-5 economies. An increase in U.S. term premium, such as during the “taper tantrum”, also results in higher long-term bond yields in all ASEAN-5 economies. The results indicate that a rise in the shadow federal funds rate and U.S. term premium could have a greater impact on Indonesia and the Philippines. Greater global risk aversion proxied by the VIX has a mixed effect on long rates, with a rise in the VIX increasing yields in Indonesia and the Philippines while lowering yields in Thailand, probably reflecting the greater home bias of Thai financial institutions. Robust fundamentals such as stronger current account balances and lower public debt tend to keep bond yields down. Expectations of currency depreciation can also drive bond yields higher. Interestingly, better growth expectations often result in lower bond yields than vice versa, suggesting that investors may see better growth prospects as a sign of improved credit worthiness rather than just a cyclical consideration. Overall, the susceptibility of long-term bond yields to global factors is consistent with the high degree of foreign participation in the ASEAN-5 economies, with foreign portfolio capital flows being a key channel of spillovers, albeit with expectations and domestic residents continuing to play a significant role.¹⁰

• Retail bank rates. Spillovers of global factors to retail bank rates in the ASEAN-5 countries were investigated following the approach of Ricci and Shi (2016) by estimating the domestic and global determinants of both deposit and loan rates.¹¹ In addition, the specification allows for liquidity effects and rigidities in interest rate transmission. The results indicate that global financial factors significantly affect bank behavior in the ASEAN-5 economies except possibly in the case of Thailand.¹² However, the domestic policy rate and liquidity conditions (measured by the deviation of reserve money from a Hodrik-Presscot trend) also matter, affirming the important role of domestic monetary policy and liquidity management operations in influencing the credit cycle.

Table 3.

Determinants of Sovereign Bond Yields ^{1/ 2/}

(10-year government bond)

^1/*** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 3.

Determinants of Sovereign Bond Yields ^{1/ 2/}

(10-year government bond)

	Domestic Factors					External Factors
	Debt to GDP ratio	Expected GDP	Inflation	Current account balance in percent of GDP (-1)	Expected exchange rates (1-year forecast)	Share of foreign holdings in total LCY government bonds	VIX	Shadow Federal funds rate	U.S. term premium
Indonesia	0.042629 −0.026855	−1.580214 *** −0.711379 *	0.224828 *** 0.231493 ***	0.093751	0.000305 **	−0.113651 ***	0.050833 ** 0.045873 ***	0.256726 *** 0.207543 ***	0.716516 *** 0.69498 ***
Malaysia	0.002688 0.00418	−0.141494 * 0.012942	0.054183 ** 0.042216	−0.018823	0.387852	−0.002034	0.000229 0.0000795	0.076478 *** 0.030623	0.188854 *** 0.178108 **
Philippines	0.070096 ** 0.030954	−0.93981 *** −1.150746 ***	0.091955 0.148232 **	−0.11592 * 0.006988	−0.14362 *** 0.022972		0.015583 0.027005 *	0.282424 *** 0.348581 ***	0.377099 ** 0.224802 **
Singapore	−0.001078 −0.000881	−0.06179 −0.056548	−0.004983 −0.046091 **	0.006988			−0.007334 0.007166	0.15361 *** 0.14116 ***	0.221407 *** 0.263296 ***
Thailand	−0.092427 * −0.147288 ***	0.028645 −0.102066	0.098888 ** 0.118716 ***	−0.028399 *	0.075183 ***	0.012005	−0.022071 * −0.024512 **	0.089696 ** 0.034472	0.210694 ** 0.178285 ***

^1/*** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

View Table

Table 3.

Determinants of Sovereign Bond Yields ^{1/ 2/}

(10-year government bond)

	Domestic Factors					External Factors
	Debt to GDP ratio	Expected GDP	Inflation	Current account balance in percent of GDP (-1)	Expected exchange rates (1-year forecast)	Share of foreign holdings in total LCY government bonds	VIX	Shadow Federal funds rate	U.S. term premium
Indonesia	0.042629 −0.026855	−1.580214 *** −0.711379 *	0.224828 *** 0.231493 ***	0.093751	0.000305 **	−0.113651 ***	0.050833 ** 0.045873 ***	0.256726 *** 0.207543 ***	0.716516 *** 0.69498 ***
Malaysia	0.002688 0.00418	−0.141494 * 0.012942	0.054183 ** 0.042216	−0.018823	0.387852	−0.002034	0.000229 0.0000795	0.076478 *** 0.030623	0.188854 *** 0.178108 **
Philippines	0.070096 ** 0.030954	−0.93981 *** −1.150746 ***	0.091955 0.148232 **	−0.11592 * 0.006988	−0.14362 *** 0.022972		0.015583 0.027005 *	0.282424 *** 0.348581 ***	0.377099 ** 0.224802 **
Singapore	−0.001078 −0.000881	−0.06179 −0.056548	−0.004983 −0.046091 **	0.006988			−0.007334 0.007166	0.15361 *** 0.14116 ***	0.221407 *** 0.263296 ***
Thailand	−0.092427 * −0.147288 ***	0.028645 −0.102066	0.098888 ** 0.118716 ***	−0.028399 *	0.075183 ***	0.012005	−0.022071 * −0.024512 **	0.089696 ** 0.034472	0.210694 ** 0.178285 ***

^1/*** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 4.

Determinants of Deposit Rates ^{1/ 2/}

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 4.

Determinants of Deposit Rates ^{1/ 2/}

	Domestic Factors			External Factors
	Policy rate	Reserve money gap	Deposit interest rate (-1)	VIX	Federal funds rate	U.S. term premium
Indonesia	0.029085 *	−0.000000515	0.931956 ***	−0.001099	0.009085	0.031518
	0.147722 ***	−0.00000331 **		−0.010371 **	0.39781 ***	0.623272 ***
Malaysia	0.034628 ***	−0.000000819	0.935265 ***	−0.001194 ***	0.002909	0.010076 **
	0.056984	0.0000124 ***		−0.003314*	0.093482 ***	0.075773 ***
Philippines	0.087721 *	−0.000000347	0.873285 ***	0.000755	−0.012197	0.034243
	0.794831 ***	−0.00000284 ***		−0.005019	−0.114371 **	0.274831 ***
Singapore	0.001507	0.00000029	0.937824	0.0000563 ***	0.00125	−0.000491
	0.020551 ***	−0.00000035		0.001726 ***	0.020946 ***	0.013999 ***
Thailand	0.046608 *	0.000158	0.881762 ***	−0.002467	0.002499	0.013047
	0.317694 ***	−0.0000239		−0.010069 ***	0.074641 ***	0.038719

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

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Table 4.

Determinants of Deposit Rates ^{1/ 2/}

	Domestic Factors			External Factors
	Policy rate	Reserve money gap	Deposit interest rate (-1)	VIX	Federal funds rate	U.S. term premium
Indonesia	0.029085 *	−0.000000515	0.931956 ***	−0.001099	0.009085	0.031518
	0.147722 ***	−0.00000331 **		−0.010371 **	0.39781 ***	0.623272 ***
Malaysia	0.034628 ***	−0.000000819	0.935265 ***	−0.001194 ***	0.002909	0.010076 **
	0.056984	0.0000124 ***		−0.003314*	0.093482 ***	0.075773 ***
Philippines	0.087721 *	−0.000000347	0.873285 ***	0.000755	−0.012197	0.034243
	0.794831 ***	−0.00000284 ***		−0.005019	−0.114371 **	0.274831 ***
Singapore	0.001507	0.00000029	0.937824	0.0000563 ***	0.00125	−0.000491
	0.020551 ***	−0.00000035		0.001726 ***	0.020946 ***	0.013999 ***
Thailand	0.046608 *	0.000158	0.881762 ***	−0.002467	0.002499	0.013047
	0.317694 ***	−0.0000239		−0.010069 ***	0.074641 ***	0.038719

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 5.

Determinants of Lending Rates ^{1/ 2/}

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

Table 5.

Determinants of Lending Rates ^{1/ 2/}

	Domestic Factors			External Factors
	Policy rate	Reserve money gap	Lending interest rate (-1)	VIX	Federal funds rate	U.S. term premium
Indonesia	0.071949 ***	−0.00000037	0.952197 ***	0.001855	−0.015185	0.00625
	0.100323	−0.00000867 ***		0.010488	0.688684 ***	0.970761 ***
Malaysia	0.01626	0.00000116	0.913686 ***	−0.001385	0.033509 ***	0.024631 **
	0.040728	0.0000139 ***		0.009755 ***	0.380285 ***	0.232558 ***
Philippines	0.305045 ***	−0.00000036	0.692695 ***	0.007808	0.030319	0.228821 ***
	1.053698 ***	−0.00000245 **		0.021974 ***	0.070293	0.78514 ***
Singapore	−0.003308	−0.000000474 **	0.927270 ***	0.0000614	0.001591	0.001380
	−0.029163 ***	−0.00000134 **		0.000581 ***	0.005802 **	0.008991 ***
Thailand	0.148981 ***	−0.000034	0.811153 ***	−0.003455 ***	−0.042286 ***	0.003074
	0.692007 ***	−0.001009**		0.000294	−0.218023 ***	−0.118202 ***

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

View Table

Table 5.

Determinants of Lending Rates ^{1/ 2/}

	Domestic Factors			External Factors
	Policy rate	Reserve money gap	Lending interest rate (-1)	VIX	Federal funds rate	U.S. term premium
Indonesia	0.071949 ***	−0.00000037	0.952197 ***	0.001855	−0.015185	0.00625
	0.100323	−0.00000867 ***		0.010488	0.688684 ***	0.970761 ***
Malaysia	0.01626	0.00000116	0.913686 ***	−0.001385	0.033509 ***	0.024631 **
	0.040728	0.0000139 ***		0.009755 ***	0.380285 ***	0.232558 ***
Philippines	0.305045 ***	−0.00000036	0.692695 ***	0.007808	0.030319	0.228821 ***
	1.053698 ***	−0.00000245 **		0.021974 ***	0.070293	0.78514 ***
Singapore	−0.003308	−0.000000474 **	0.927270 ***	0.0000614	0.001591	0.001380
	−0.029163 ***	−0.00000134 **		0.000581 ***	0.005802 **	0.008991 ***
Thailand	0.148981 ***	−0.000034	0.811153 ***	−0.003455 ***	−0.042286 ***	0.003074
	0.692007 ***	−0.001009**		0.000294	−0.218023 ***	−0.118202 ***

^1/Short-term interest rate (SIBOR, 3 months) was used for the Singapore’s policy rate variable. *** significant at .01 level; **significant at .05 level; *significant at .10 level.

^2/The coefficients reflect the marginal increase in interest rates in percent of a 1 percentage point rise in the explanatory variables.

12. An active operational framework that aligns market conditions with the announced policy stance have helped to maintain the effectiveness of policy rate transmission in most periods despite the rising sensitivity to global factors. Central bank operations in the ASEAN-5 economies have generally aligned market rates with the announced interest rate corridor (see Figure 8), except in the case of Philippines where, until recently, short-term money market rates were much lower than the policy rate corridor reflecting the difficulty that the BSP encountered in mopping up excess liquidity deriving from the surge in capital inflows during 2009–2011 given the limited instruments at its disposal.¹³In Indonesia’s case, the overnight interbank rate was effectively at the bottom of the policy interest rate corridor again reflecting the challenges that Bank Indonesia had in ramping up open market operations with limited instruments in the context of UMPs in AEs. An effectively implemented monetary operation framework supports the functioning of money markets, allowing banks to predictably place surplus liquidity with, and obtain short-term funding from each other or the central bank at rates that are related to the policy rates. The continued significance of policy rates and liquidity conditions in determining retail bank rates highlight the importance of active liquidity management in a world of excess global liquidity.

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Policy and Market Interest Rates

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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13. Managing the global financial cycle is a key challenge for ASEAN-5 monetary frameworks. The results above suggest the existence of a global financial cycle emanating from changes in U.S. monetary policy and global risk aversion that drives domestic financial conditions in the ASEAN-5 economies. The results are consistent with the findings of IMF (2014c) that show a high sensitivity of EME asset prices to global financial conditions. Our findings extend this literature by showing that the sensitivity to global factors extend to retail bank rates as in Ricci and Shi (2016), the main channel of monetary transmission in the ASEAN-5 economies. This puts the traditional “trilemma” view of the independence of monetary policy with flexible exchange rate into question as flexible exchange rate alone is unable to fully insulate economies from the global financial cycle, when capital account is highly open and financial flows are driven by monetary conditions in the U.S. and can be highly volatile (Rey 2013). In addition, the transmission of global financial factors through domestic asset prices suggests a potential amplification of global financial cycles through “financial accelerator” effects on the real economy that would be important to take into account.¹⁴ IMF (2014c) shows that financial deepening lowers the sensitivity of EME equity and bond prices to global financial factors; the results for the foreign exchange market are somewhat weaker. That said, generalized reductions in global interest rates and loose liquidity conditions have increased the risk of asset price and credit boom and bust cycles in Asia (see Gupta and others, 2010), raising financial stability concerns and disorderly adjustment to sudden stops in capital inflows. In this light, the next section will assess the effectiveness of traditional monetary policy, as well as the role of MPPs in maintaining financial stability (IMF 2014a), and CFMs/FX intervention and exchange rate flexibility in managing volatile capital flows in the ASEAN-5 economies.

A. Monetary Policy

14. Estimates of Taylor rule reaction functions are used to gauge monetary policy responses and drivers (see Appendix II). The standard Taylor rule uses the output gap and inflation (or deviation from its target) to describe policy interest rate settings. In the case of Singapore, the rule is modified to reflect the use of the nominal effective exchange rate as the main instrument for monetary policy.¹⁵ Augmentation of the Taylor rule permits analysis of the relevance of other variables such as the exchange rate, U.S. interest rates, and global uncertainty in monetary policy settings in the ASEAN-5 economies. This paper uses thick estimation techniques that avoid the selection of a single equation and instead involves estimation of all plausible combinations of potential explanatory variables (Granger and Jeon, 2004). The approach thus provides insights as to whether a variable of interest generally guides decisions rather than its significance in one single equation.

15. The Taylor rule estimations fit the data well and provide valuable insights on policy directions.¹⁶ The lagged dependent variable plays a large role with a coefficient of 0.6 in Malaysia and close to 1.0 in the Philippines indicating a strong preference for interest rate smoothing. The analysis confirms the role of expected inflation in guiding policy rate settings in all countries with the coefficient estimates on expected inflation exceeding those on either inflation or core inflation. The inflation rate has the greatest relevance in Thailand, with statistically significant coefficients on average for all three variables and coefficient value in excess of 1 in response to increases in either core or expected inflation. On the other hand, Malaysia—a non-inflation targeter—appears least responsive to changes in inflation. The output gap is insignificant except in the case of Malaysia, where a negative output gap of 1 percentage point is associated with a 25 basis point reduction in the policy interest rate. This finding, along with the results on the inflation rate, points to a greater emphasis on output and employment rather than inflation in Malaysia.

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Taylor Rule Estimations for ASEAN-5 ^1/

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

^1/ The bars indicate a two standard deviation range for estimated coefficients based on a thick estimation technique that uses bootstrap aggregation to combine information from the estimation of a large number of plausible empirical policy rule models. Note that the dependent variable for Singapore is the percentage change in the nominal effective exchange rate. VIX coefficients are multiplied by the standard deviation of the VIX from 1990:Q1 to 2015:Q3.

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16. Nontraditional factors also play a role in the ASEAN-5 economies. In previous studies, the exchange rate has been found to have an impact on the monetary policy decisions even in EMEs with IT regimes (Ostry and others, 2012). The coefficient estimates are on aggregate insignificant, suggesting little role the exchange rate played in setting the policy interest rate in the ASEAN countries. Looking at the possible role of global shocks, a dummy variable for the global financial crisis is statistically significant with a large negative sign, ranging between 30 bps for Malaysia to 75 bps for Indonesia, and captures the role of external factor in affecting policy rates. Alternatively, the VIX was found to be statistically significant and suggests that a 30 point increase in the VIX (e.g., as in September 2011) has been associated with a decline in policy rates of 10–45 bps.

17. The role of U.S. interest rates in policy reaction functions are explored in more detail given the finding of U.S. interest rate spillovers on domestic financial conditions. Higher U.S. short-term interest rates are generally associated with higher policy rates in the ASEAN-5 countries, and this is the case for both the federal funds rate as well as the shadow-short term rate. The results suggested that U.S. shadow interest rates associated with UMPs have put significant downward pressure on policy interest rates in the ASEAN-5 economies (Figure 10). That said, there appears to be some heterogeneity in the response, with the estimated impact smaller in the more financially developed markets of Malaysia and Singapore, that may be better able to insulate asset markets from volatile capital flows. This deviation from more traditional Taylor rule implied policy rates in the ASEAN-5 countries suggests a potential structural break (Hofmann and Bogdanova, 2012) to a “new normal.”

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Impact of U.S. Monetary Policy ^1/

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

^1/ The impact of U.S. interest rates depicted is the coefficient (marginal impact) on the shadow U.S. interest rate in the regression multiplied by the change in the shadow U.S. rate in the period, giving a measure of the implied change in policy rates.

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B. The Global Financial Cycle and External Adjustment

18. Gross capital outflows have smoothed the adjustment to the global financial cycle while reserves have played an important buffer role (IMF 2016a). Behind the global financial cycle, the contributions from capital inflows and outflows vary sizably over time in the ASEAN-5 economies. IMF (2013d) argues that EMEs can improve the management of the global capital flow cycle through development of their financial markets, which fosters private sector outflows during nonresident inflow episodes that can help stabilize net capital flows.¹⁷ In addition, the buildup and use of a reserve buffer can help counteract capital outflow episodes in EMEs as observed in 2010–15 (IMF 2016a). The motivation for the accumulation of reserves in the ASEAN-5 economies was based on their experience during the AFC and perceived benefits of building an adequate reserve buffer to shield the economy from the liquidity effects of volatile capital flows. Reserve levels were in some cases below or at the lower bound of the Fund reserve adequacy metric range at the beginning of the great moderation but were gradually built up to comfortable levels prior to the GFC (see Figure 11). At the same time, they moved towards a more flexible exchange rate regime to enhance monetary policy autonomy (see “trilemma” triangles in Section I) and role of the exchange rate as a shock absorber (see below) in line with Fund policy positions. Malaysia is one of the EMEs with deep financial markets which were able to intermediate most of the inflows through financial institutions investments abroad (Figure 11). The accumulation of reserves during periods of large gross capital inflows in 2002–2007 and in 2009–2011 was mainly on account of the large current surpluses and the short-term capital inflows which were mopped up by Bank Negara bills to shield the financial system from its liquidity impact and eventual outflow. During periods of large gross capital outflows and declining gross capital inflows in 2008–2009 and 2013–2015, Bank Negara ran down its FX reserves and stock of Bank Negara bills to accommodate the outflows alongside exchange rate depreciation in order to buffer the shock on the economy. As a result, the current account remained in surplus during the whole period (although less so in the recent period due to the decline in commodity prices). Indonesia, the Philippines, and Thailand ran current account deficits in response to large gross capital inflows in the pre-AFC period, but managed to isolate the current account from fluctuations in gross capital inflows thanks to counteracting gross capital outflows and reserve accumulation in 2003–2007, and mainly through reserve accumulation in the UMP period (2010–2012). For Singapore, most of the variation in gross capital inflows is offset by similar variations in gross capital outflows, with little action in the current account or reserve accumulation, as would be expected from a financial center.

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Global Financial Cycle: Financial and Real Adjustment in ASEAN-5

(In percent of GDP)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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19. Since 2013, gross capital inflows have moderated, and the ASEAN-5 economies have reduced the pace of reserve accumulation or deccumulated as in other EMEs (Figure 12 and IMF 2016a). The fact that reserve accumulation slowed down in tandem with diminished capital inflows (or turned into reserve losses in some countries seeing outflows) also has a positive side: by facilitating the repayment of residents’ foreign-currency liabilities, the sale of foreign assets could reduce balance sheet fragilities coupled with the growing ability to issue debt denominated in local currency in the ASEAN-5 economies (see Figure 13). With strengthened domestic balance sheets, a currency depreciation can play its traditional role of smoothing adjustment to external shocks. In fact, the ASEAN-5 relied more on currency depreciation than reserves changes in 2013–15 compared to previous inflow and outflow episodes (see Figure 12 and Appendix III). This also meant that the ASEAN-5 economies’ external gaps based on the External Balance Assessment (EBA) approach of the Fund narrowed over the two global financial cycles and were largely closed during the outflow episodes.¹⁸ The greater exchange rate flexibility in the ASEAN-5 economies documented in Section I may also have mitigated the slowdown in capital inflows as shown in IMF (2016a) where more flexible exchange rate regimes reduce the share of the total variance in capital inflows explained by common global factors.¹⁹ In general, the reserve buffers built up during the great moderation and UMP period were drawn down, in some cases close to the lower bound of the Fund’s reserve adequacy metric range (Indonesia and Malaysia), albeit with the Philippines and Thailand continuing to maintain reserves above the range, indicating a self insurance motive that goes beyond levels implied by cross country experiences in some cases. This may be seen as an endogenous response to the experience of the AFC. In such a case, it would also be important to consider the tradeoff between self-insurance and the cost of holding reserves.

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International Reserve Buffers

Changes in Exchange Rates and International Reserves (In percent)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: Bloornbeig L P.;and IMF staff estimates.

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FX Denominated Debt in Asia and Other Selected Economies 1/

(In percent of GDP)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: Dealogk Data Co. Ltd: and IMF staff estimates.^1/ Bonds and syndicated bans.

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20. The ASEAN-5 countries are not among those with the highest degree of FX intervention, except for Singapore (Figure 14).²⁰ Indonesia’s degree of exchange rate management is the lowest and is comparable to that of some advanced economies, like Japan. Philippines and Thailand follow, with slightly higher degree of exchange rate management. Malaysia is around the median of the sample between Russia and Argentina. Finally, Singapore has a very high degree of exchange rate management, comparable to that of China, which is not surprising given its exchange rate based approach of IT.

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Degree of Exchange Rate Management

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: The figure reports a measure ${\rho }_j={\sigma }_j^{NFA}/({\sigma }_j^{NFA}+{\sigma }_j^S)$ where ${\sigma }_j^{\mathrm{\Delta }NFA}$ and ${\mathrm{\Delta }}_j^{\mathrm{\Delta }S}$ denote the standard deviations of changes in net foreign assets and in nominal exchange rate, respectively. Gray bars correspond to countries with de jure pegs for most of the sample, and rest of the bars otherwise.

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21. ASEAN-5 central banks have generally sterilized their FX intervention. To measure the intensity of sterilization in the ASEAN-5 economies, a sterilization coefficient (β) is estimated following the approach of Aizenman and Glick (2008). This coefficient is estimated using one month extended and 60-month rolling windows, where β=-1 represents full sterilization of reserve changes; β=0 implies no sterilization; and -1<β<0, indicates partial sterilization. Average sterilization coefficients in the ASEAN-5 economies have remained close to β=-1 in the post-AFC period (Figure 15 and Table 6). In general, the ASEAN-5 countries have attempted to fully sterilize their FX intervention even during the UMP period (albeit with temporary periods of partial sterilization in Indonesia, Malaysia and the Philippines) when the accumulation of foreign reserves was especially strong and sterilization may have attracted greater capital inflows.

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Sterilization Coefficients 1/–3/

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Source: IMF staff estimates.^1/The extent of sterilization coefficient (β) is estimated following the approach of Aizenmann and Glick (2008), with simple regression of the change in net domestic assets (NDA) on the change of net foreign assets (NFA), scaled by the level of reserve money stock a year (or 12 months) ago, as: dNDA/RM(-12)=a+β*dNFA/RM(-12)+e.2/ Red line: one month extended window; Blue: 60 month rolling window for ASEAN-4, 80-month rolling window for Singapore.^3/Sample period for Philippines, Indonesia and Thailand: monthly data from 2001–2015; for Malaysia and Singapore: monthly data from 2002–2015.4/ Average sterilization coefficient using one-month extended window in the following periods: pre-GFC (starting January 2005 or onward data available up to August 2008), GFC (September 2008 to March 2009), post-GFC (April 2009 to April 2013) and taper tantrum (May 2013 to December 2013).

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22. The benefits of holding reserve buffers need to be weighed against its costs.²¹ The marginal opportunity cost of reserve buffers can be estimated as the cost of rolling over FX positions and thus equates to departures from uncovered interest parity (UIP) following Adler and Mano (2016).²² In the sample considered, the ex-post marginal costs of FX intervention, as represented by departures from UIP, have been sizeable. From a policy perspective, however, ex-post marginal costs are not a relevant consideration because central banks cannot anticipate unexpected shocks that may move costs significantly when deciding whether to intervene in FX markets. Adler and Mano (2016), estimate more policy relevant ex-ante costs or expected UIP departures in several ways using both survey-based expectations and statistical model estimates. The average ex-ante total costs for Indonesia, Philippines, Thailand, Malaysia, and Singapore are 0.6, 0.7, 0.9, 1.0 and 1.3 percent of GDP, respectively. The total cost for the median EME, on the other hand, is 0.5 percent of GDP. Total costs of FX reserve buffers for ASEAN-5 countries seem to be in line with a broad sample of countries, albeit slightly on the high side (Figure 16).

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Average Total Cost of FX Intervention, 2002–13

(In percent of GDP)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Source: IMF, International Financial Statistics; and IMF staff estimates.1/ Range between the minimum and maximum estimated ex-ante country-average across different methods.2/ Average of ex-ante country averages across methods.3/ Ex-post country average.

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Table 6.

Sterilization Coefficients ^1/

^1/Average sterilization coefficient using one-month extended window in the following periods: pre-GFC (starting January 2005 or onward data available up to August 2008), GFC (September 2008 to March 2009), post-GFC (April 2009 to April 2013) and taper tantrum (May 2013 to December 2013).

Table 6.

Sterilization Coefficients ^1/

	Pre-GFC	GFC	Post-GFC	Taper Tantrum
Indonesia	−0.957	−0.901	−0.838	−0.824
Malaysia	−0.933	−0.914	−0.871	−0.839
Philippines	−0.806	−0.709	−0.765	−0.833
Singapore	−0.989	−0.981	−1.000	−1.004
Thailand	−1.000	−1.000	−1.000	−1.000

^1/Average sterilization coefficient using one-month extended window in the following periods: pre-GFC (starting January 2005 or onward data available up to August 2008), GFC (September 2008 to March 2009), post-GFC (April 2009 to April 2013) and taper tantrum (May 2013 to December 2013).

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Table 6.

Sterilization Coefficients ^1/

	Pre-GFC	GFC	Post-GFC	Taper Tantrum
Indonesia	−0.957	−0.901	−0.838	−0.824
Malaysia	−0.933	−0.914	−0.871	−0.839
Philippines	−0.806	−0.709	−0.765	−0.833
Singapore	−0.989	−0.981	−1.000	−1.004
Thailand	−1.000	−1.000	−1.000	−1.000

^1/Average sterilization coefficient using one-month extended window in the following periods: pre-GFC (starting January 2005 or onward data available up to August 2008), GFC (September 2008 to March 2009), post-GFC (April 2009 to April 2013) and taper tantrum (May 2013 to December 2013).

C. MPPs, CFMs, and the Financial Cycle

23. Capital inflows present opportunities, but they can also pose stability risks. Capital inflows, if channeled effectively, represent an opportunity to address long-standing investment needs, such as in infrastructure (Sahay and others, 2015). However, capital inflows, especially short-term portfolio flows, need to be managed carefully in order to avoid macroeconomic and financial stability risks.

24. Capital flows can give rise to financial stability risks through different channels (IMF 2014a), including: (i) increases in short-term wholesale funding of the banking system; (ii) increases in foreign currency funding of the financial system; (iii) contributions of capital inflows to local credit booms and asset price appreciation; and (iv) credit risks from foreign currency denominated loans. While (i), (ii), and (iv) are beyond the scope of this paper, credit cycles related to capital inflows can complicate monetary management and also raise systemic risks, with implications for macroeconomic stability and the conduct of monetary policy. Asia’s economic and financial history also suggests that high liquidity growth at a time of large capital inflows increases the risk of asset price boom and bust cycles (Gupta and others, 2009) that could lead to potential feedback loops between the corporate/household sectors and banks.

25. Capital inflows and low interest rates accelerated credit growth in the ASEAN-5 economies during the UMP period, as in the rest of Asia (IMF 2015c). The ASEAN-5 economies’ strong growth performance in the aftermath of the GFC came on the back of a strong rise in private credit. However, this faster credit growth has been associated with an increase in the credit intensity of output—the change in credit-per-unit increase in GDP—pointing to a decline in the stimulative effect of credit in the post-GFC period. If the decline in credit intensity was related to purchases of existing real assets (including real estate) or to finance purchases of financial assets and reflected a greater attraction to debt in a low interest environment, it may raise the likelihood of boom bust cycles. Since episodes of rapid credit growth in Asia have been characterized by a higher incidence of crises relative to other EMEs (IMF 2011b), whether the global financial cycle has driven domestic credit booms and thus raised systemic risks in the ASEAN-5 economies is an important consideration.

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Credit Intensity of Output

(Ratio)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: BIS; IMFr, World Economic Outlook; and staff estimates.

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26. We use alternative approaches to identify credit booms in the ASEAN-5 economies. There is no single criterion to identify credit booms in the literature, so we use three different methodologies from previous studies. The first one is that of Mendoza and Terrones (2008), which looks at deviations of real credit per capita from its Hodrick-Prescott trend, identifying credit booms when the deviation from trend is larger than 1.75 times its standard deviation. The second one is that of Dell’Aricia and others (2012), which looks at deviations of credit-to-GDP from a rolling cubic trend. The last methodology is that of Chapter 3 of the IMF’s Global Financial Stability Report (GFSR) of September 2011 (IMF 2011a), which finds that increases in the credit-to-GDP ratio above 3 percent could serve as early warning of credit booms, with increases above 5 percent indicating more advanced and severe credit booms.

27. All three approaches identify credit booms prior to the AFC in all ASEAN-5 economies, but the evidence for credit booms since then is limited (see Table 7). The three methodologies identify credit booms in ASEAN-5 countries prior the AFC. However, except for Singapore, none of the methodologies show that the ASEAN-5 economies experienced credit booms in the run up to GFC or thereafter. In addition, in the case of Singapore, more weight should be given to the first two approaches because the credit to GDP ratio is very high because it is a financial center and thus the GFSR approach is more likely to find variations in its credit to GDP ratios that surpass the thresholds.

Table 7.

Heat Map on the Evidence of Credit Booms ^1/–^4/

^1/Shades of green indicate lower threshold/early warning of credit boom; shades of red indicate that credit is above upper threshold/evidence of a credit boom.

^2/Figures under Mendoza and Terrones, 2008 (M&T) refer to the deviations of log real credit per capita from its HP trend times 1.75 the trend’s standard deviation. The deviations are averaged for the sub-periods identified. Positive figures shaded in red indicate an evidence of credit boom.

^3/Figures under Dell’Ariccia and others, 2012 (D&O) refer to the average growth of credit-to-GDP ratio for the sub-periods identified. Figures shaded in green and red show ratio above the lower cut-off at 10 percent ratio and upper threshold at 20 percent ratio,

^4/Figures under the IMF’s GFSR refer to the annual change in credit-to-GDP ratio in percentage points, averaged for the sub-periods identified. Figures shaded in green and red identify change in credit-to-GDP ratio above 3 percentage points and 5 percentage points, respectively.

Table 7.

Heat Map on the Evidence of Credit Booms ^1/–^4/

	Pre-AFC (1996–97)			Pre-GFC (2007–08)			Post-GFC/UMP (2009–2012)			Post-Taper Tantrum (2013–15)
	M&T	D&O	GFSR	M&T	D&O	GFSR	M&T	D&O	GFSR	M&T	D&O	GFSR
Indonesia	0.09	−0.98	−0.53	−0.30	5.22	1.18	−0.29	3.76	0.89	−0.29	3.65	1.06
Malaysia	0.05	16.58	20.87	−0.23	1.38	1.22	−0.19	2.66	2.73	−0.17	2.27	2.59
Philippines	0.10	20.98	8.95	−0.36	2.14	0.58	−0.35	2.96	0.86	−0.24	7.51	2.57
Singapore	−0.02	7.89	6.90	−0.12	11.16	9.25	−0.12	3.98	3.84	−0.04	4.43	5.06
Thailand	0.07	12.27	17.26	−0.25	1.60	1.40	−0.22	4.83	4.74	−0.17	3.49	4.05

^1/Shades of green indicate lower threshold/early warning of credit boom; shades of red indicate that credit is above upper threshold/evidence of a credit boom.

^2/Figures under Mendoza and Terrones, 2008 (M&T) refer to the deviations of log real credit per capita from its HP trend times 1.75 the trend’s standard deviation. The deviations are averaged for the sub-periods identified. Positive figures shaded in red indicate an evidence of credit boom.

^3/Figures under Dell’Ariccia and others, 2012 (D&O) refer to the average growth of credit-to-GDP ratio for the sub-periods identified. Figures shaded in green and red show ratio above the lower cut-off at 10 percent ratio and upper threshold at 20 percent ratio,

^4/Figures under the IMF’s GFSR refer to the annual change in credit-to-GDP ratio in percentage points, averaged for the sub-periods identified. Figures shaded in green and red identify change in credit-to-GDP ratio above 3 percentage points and 5 percentage points, respectively.

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Table 7.

Heat Map on the Evidence of Credit Booms ^1/–^4/

	Pre-AFC (1996–97)			Pre-GFC (2007–08)			Post-GFC/UMP (2009–2012)			Post-Taper Tantrum (2013–15)
	M&T	D&O	GFSR	M&T	D&O	GFSR	M&T	D&O	GFSR	M&T	D&O	GFSR
Indonesia	0.09	−0.98	−0.53	−0.30	5.22	1.18	−0.29	3.76	0.89	−0.29	3.65	1.06
Malaysia	0.05	16.58	20.87	−0.23	1.38	1.22	−0.19	2.66	2.73	−0.17	2.27	2.59
Philippines	0.10	20.98	8.95	−0.36	2.14	0.58	−0.35	2.96	0.86	−0.24	7.51	2.57
Singapore	−0.02	7.89	6.90	−0.12	11.16	9.25	−0.12	3.98	3.84	−0.04	4.43	5.06
Thailand	0.07	12.27	17.26	−0.25	1.60	1.40	−0.22	4.83	4.74	−0.17	3.49	4.05

^1/Shades of green indicate lower threshold/early warning of credit boom; shades of red indicate that credit is above upper threshold/evidence of a credit boom.

^2/Figures under Mendoza and Terrones, 2008 (M&T) refer to the deviations of log real credit per capita from its HP trend times 1.75 the trend’s standard deviation. The deviations are averaged for the sub-periods identified. Positive figures shaded in red indicate an evidence of credit boom.

^3/Figures under Dell’Ariccia and others, 2012 (D&O) refer to the average growth of credit-to-GDP ratio for the sub-periods identified. Figures shaded in green and red show ratio above the lower cut-off at 10 percent ratio and upper threshold at 20 percent ratio,

^4/Figures under the IMF’s GFSR refer to the annual change in credit-to-GDP ratio in percentage points, averaged for the sub-periods identified. Figures shaded in green and red identify change in credit-to-GDP ratio above 3 percentage points and 5 percentage points, respectively.

28. The limited evidence of broad based credit booms masks pockets of sectoral imbalances. While increasing credit-to-GDP ratios can be regarded as part of financial deepening in emerging markets, a few countries in the region seem to have much higher ratios than what their GDP per capita would imply. In recent years, household debt has increased rapidly in Malaysia and Thailand, with household debt-to-GDP ratios now standing above 80 percent of GDP in both economies. Moreover, the run up in household debt was driven by mortgage lending during a period of rapid house price inflation. To assess the financial stability risks of household debt, it is important to consider the other aspects of the household balance sheets (D’Alessio and Iezzi 2013), which is beyond the scope of this study, but the trends have drawn the attention of central banks and financial regulators in the region. On the other hand, levels of corporate debt in the region appears manageable notwithstanding the rise in corporate leverage during the UMP period, although aggregate measures may mask pockets of vulnerability among a segment of corporates or a few firms that would be the focus of microprudential supervision and financial surveillance²³ (Figure 19).

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Household Debt and House Prices

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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Corporate Debt and Interest Coverage Ratio

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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29. Addressing financial stability risks of rising household leverage and the real estate price cycle would explain the broadening of the toolkit to sectoral MPPs in the ASEAN-5 economies. Updated MPP and CFM indices compiled by Zhang and Zoli (2014) show an increasing use of MPPs in the ASEAN-5 economies in the wake of the GFC (Figure 20), particularly of housing-related measures. ²⁴ While a comprehensive quantitative assessment of their effectiveness in taming the housing leverage and asset price cycles in the ASEAN-5 economies is not feasible given the limited tightening episodes and time span, a visual inspection of trends provide preliminary evidence of efficacy. In Indonesia, housing loan growth slowed from its peak of 32 percent y/y in Q3:2013 to 12 percent in Q3:2014, following the tightening of loan-to-value (LTV) ratios in June and September 2013. House price inflation in Indonesia also slowed from 13.5 percent in Q3:2013 to 6.5 percent in Q3:2014, although other macrofinancial factors and the use of CFMs may also explain the change in asset price dynamics. This is in line with the findings of Zhang and Zoli (2014) that CFMs and housing-related MPPs have been effective in reducing housing price inflation in countries²⁵ that have used them more intensively. Malaysia and Thailand show patterns of a countercyclical response, with the loosening of MPPs following sharp declines in the growth of housing loans and prices during the GFC period, and tightening of measures (e.g. LTVs, real property gain tax, mortgages cap, etc.) during the upsurge in property credit and prices in the UMP period. House price growth clearly slowed following the tightening of MPPs in Malaysia alongside a relatively constant level of housing loan growth, although the tightening of domestic financial conditions post-taper tantrum may have also played a role. The impact of MPPs on the real estate cycle is less visible in Thailand but one cannot rule out a counterfactual scenario where real estate prices and household leverage would have continued to rise if MPPs were not tightened. Singapore shows a more typical pattern in the use of MPPs as in the rest of Asia and EMEs with a progressive tightening of mainly housing-related measures (Zhang and Zoli 2014) and a sharp fall in housing loans and prices.²⁶ The Philippines did not formally impose any MPPs to ease the pace of real estate loan growth but enhanced monitoring of banks’ real estate exposures and introduced regular stress testing of housing loan portfolios that may have indirectly slowed house price appreciation and construction/real estate loan growth through moral suasion and enhanced supervision. Overall, the targeted actions focused on household debt and real estate prices with limited evidence of generalized credit booms, suggests that the ASEAN-5 central banks used MPPs primarily for financial stability considerations.

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MPP, Housing Loans, and House Prices

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Source: MPP and CFM indices from Zhang and Zoli (2014); and central bank websites/annual reports.

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30. The use of CFM measures has been geared towards managing volatile capital flows and systemic risks posed by the flows.²⁷ The ASEAN-5 economies have relied mostly on domestic prudential tools, and the use of capital flow management measures was largely limited to reserve requirements on FX deposits, except for Indonesia and Thailand, where restrictions on bond holding period or withholding tax for foreigners were implemented. There is some evidence that those measures may have been effective in reining in the rapid rise in foreign participation in local currency bond markets (Figure 21), though vulnerability to shifts in foreign portfolio sentiment remained high. The Philippines also imposed a higher differential capital charge on domestic and foreign banks’ NDF exposures as a macroprudential tool to reduce systemic risks of exchange rate fluctuations, that may also be classified as a CFM measure that significantly reduced NDF positions of onshore banks. In general, the limited reliance on CFMs in the ASEAN-5 economies may have reflected their negative experiences with such measures in the past and mixed views of their effectiveness in the literature (see Zhang and Zoli, 2014), as well as a more selective and targeted approach that focused on changing the composition of capital flows to less volatile components would be more effective (Sahay and others, 2014).

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CFMs, Offshore Implied Yields, and Foreign Participation in Local Currency Government Bond Markets

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: CFM indices from Zhang and Zoli (2014); central bank websites/annual reports; ADB Asian Bond Online; Philippine Bureau of Treasury (BTr); and Bloomberg L.P. BTr’s data refers to the nonresidents’ share of government securities holdings in the Philippines under custodial accounts of banks and may differ from the IMF’s Coordinated Portfolio Investment Survey which includes offshore bonds in global peso notes and FX denominated Republic of Philippines bonds.

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A. Policy Responses to the GFC, Taper Tantrum, and Renminbi Adjustment

31. ASEAN-5 economies used a wide range of policy tools to supplement monetary policy in addressing market pressures and its economic impact, including fiscal measures, MPPs, CFMs, FX intervention, and liquidity provision measures into money markets (Table 8). In particular, while all countries raised their policy rates during the AFC to support their external positions, they eased their policy rates in the aftermath of the global financial crisis to support growth (Figure 22). By comparison, only Indonesia raised its policy rates during the taper tantrum to support external position, while Malaysia and the Philippines subsequently tightened modestly for domestic stability considerations. Singapore and Thailand gradually eased their monetary policy stance from 2011–2012 reflecting the weakening economic outlook. During the 2015 summer turbulence, policy rates were left unchanged in all ASEAN-5 economies, as policymakers had to weigh concerns about capital flows reversals that were largely confined to portfolio equity flows against those of slowing economic activity. However, only in January 2016 did Indonesia start loosening monetary policy to support domestic demand.

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ASEAN-5: Policy Interest Rates

(In percent)

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

Sources: Haver Data Analytics; and IMF staff estimates.

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Table 8.

Policy Tools Used During the GFC and Taper Tantrum

Sources: IMF, ASEAN-5 countries’ staff report for the Article IV consultation.

^1/Unlike the other ASEAN-5, Singapore does not use the policy rate as main monetary policy instrument. Instead, it uses the exchange rates corridor band.

Table 8.

Policy Tools Used During the GFC and Taper Tantrum

	Indonesia			Malaysia			Philippines			Singapore			Thailand
	GCF		Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015
Policy rate 1/	Lowered	Raised	Unchanged	Lowered	Unchanged	Unchanged	Lowered	Lowered	Unchanged	Unchanged					Lowered	Unchanged
Exchange rate corridor band 1/										Recentered to validate a weaker currency 1/	Unchanged	Unchanged
Exchange rate depreciation		Yes		Yes(?)				Yes					Yes
Drawdown of Reserves								Yes
Macroprudential policy		Tightened LTV for motor vehicles and residential properties			Imposed limit on mortgage term, max tenure of financing for						Restricted motor vehicle and public housing loans, measures of property loans; imposed limits on total debt servicing ratio
Reserve requirements	Lowered	Raised		Lowered			Lowered
Capital flow measures		Shortened minimum holding period for central bank bills						Imposed limits on banks’ NDF
FX interventions	Yes	Yes	Yes	Yes		Yes				Yes					Yes
Liquidity provision measures	Yes	Yes		Yes			Yes
Expansion of deposit insurance coverage	Yes			Yes			Yes			Yes			Yes
Expansion of eligible collateral for short-term financing	Yes
Loan guarantees													Yes
Fiscal policy	Expansive	Reduced fuel subsidies		Expansive			Expansive			Expansive			Expansive
Other measures		Swap arrangements with other countries; contingent loans

Sources: IMF, ASEAN-5 countries’ staff report for the Article IV consultation.

^1/Unlike the other ASEAN-5, Singapore does not use the policy rate as main monetary policy instrument. Instead, it uses the exchange rates corridor band.

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Table 8.

Policy Tools Used During the GFC and Taper Tantrum

	Indonesia			Malaysia			Philippines			Singapore			Thailand
	GCF		Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015	GCF	Taper Tantrum	Summer 2015
Policy rate 1/	Lowered	Raised	Unchanged	Lowered	Unchanged	Unchanged	Lowered	Lowered	Unchanged	Unchanged					Lowered	Unchanged
Exchange rate corridor band 1/										Recentered to validate a weaker currency 1/	Unchanged	Unchanged
Exchange rate depreciation		Yes		Yes(?)				Yes					Yes
Drawdown of Reserves								Yes
Macroprudential policy		Tightened LTV for motor vehicles and residential properties			Imposed limit on mortgage term, max tenure of financing for						Restricted motor vehicle and public housing loans, measures of property loans; imposed limits on total debt servicing ratio
Reserve requirements	Lowered	Raised		Lowered			Lowered
Capital flow measures		Shortened minimum holding period for central bank bills						Imposed limits on banks’ NDF
FX interventions	Yes	Yes	Yes	Yes		Yes				Yes					Yes
Liquidity provision measures	Yes	Yes		Yes			Yes
Expansion of deposit insurance coverage	Yes			Yes			Yes			Yes			Yes
Expansion of eligible collateral for short-term financing	Yes
Loan guarantees													Yes
Fiscal policy	Expansive	Reduced fuel subsidies		Expansive			Expansive			Expansive			Expansive
Other measures		Swap arrangements with other countries; contingent loans

Sources: IMF, ASEAN-5 countries’ staff report for the Article IV consultation.

^1/Unlike the other ASEAN-5, Singapore does not use the policy rate as main monetary policy instrument. Instead, it uses the exchange rates corridor band.

32. A differential response was observed across countries and episodes depending on the circumstances (Table 8). During the GFC, Indonesia, Malaysia and the Philippines lowered banks’ reserve requirements and expanded liquidity provision measures to preserve orderly money market conditions. Moreover, all ASEAN-5 economies expanded depositor insurance guarantees. Fiscal stimulus packages were also implemented to stimulate growth. In contrast, during the taper tantrum episode, Indonesia—the ASEAN-5 country under the most pressure—had to give priority to stability over supporting economic activity. Reserve requirements and the loan-to-value ratio were tightened to contain credit growth while the exchange rate and the long-term bond yields were allowed to move freely after an initial period of containment. Fiscal policy was also tightened, with an average 33 percent increase in subsidized fuel prices, to address external and fiscal imbalances. Conversely, the minimum holding period for central bank bills were shortened to increase their liquidity and attract more foreign inflows. During the summer of 2015, reserve requirements were left unchanged, but were reduced in December and January in Indonesia and Malaysia, respectively.

33. Foreign reserves were used as a buffer, coupled with greater exchange rate flexibility, to help cushion the economy and avoid disorderly market conditions. All ASEAN-5 currencies came under severe pressure and depreciated significantly during the GFC, letting the exchange rate act as a shock absorber. The net capital outflows during the taper tantrum was not as large as in the GFC, but there was greater differentiation by the markets of the strength of the countries’ macro fundamentals, with Indonesia, in particular, facing severe pressure owing to its twin deficits, prompting more FX intervention to avoid disorderly market conditions (Figure 24, IMF 2015f). Moral suasion in the FX market and purchases of government securities by Bank Indonesia were also reduced to allow for price adjustments with greater transparency on market interventions and enhanced communications with market participants. During the 2015 summer turbulence, all ASEAN-5 economies suffered from financial market volatility particularly in equity markets. However, the foreign reserve drawdown was most pronounced in Indonesia and Malaysia, the two commodity exporters that were most affected by the oil price collapse and required an external adjustment to smooth the external shock, with reserves falling close to the Fund’s reserve adequacy metric. Overall, greater exchange rate flexibility, helped smooth “excess” volatility and/or preserve orderly market conditions during the turmoil.²⁸

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Foreign Exchange Responses to Capital Outflow Episodes

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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Reaction to Disorderly Market Conditions

Citation: IMF Staff Country Reports 2016, 176; 10.5089/9781498308748.002.A001

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34. In some circumstances, the motivation for FX intervention may be to mitigate capital flow shocks and counter potentially disorderly market conditions. In particular, in periods of excessive currency volatility the exchange rate can stop operating as a shock absorber and may become a shock amplifier, including through balance sheet concerns. FX intervention can help counter such conditions; it can be sustained but needs to be two-sided as observed in the ASEAN-5 countries. Excessive intervention however is not without risks, especially to the extent that frequent use of FX intervention may undermine the clarity and credibility of the monetary policy framework, although good communication and enhanced transparency could help clarify objectives. There is also the related question of the consistency of the overall policy mix, and the need to ensure that objectives are congruent and the use of FX intervention does not substitute for other needed policy changes. FX intervention benefits in terms of dampening shocks should also be weighed against their potential costs to the credibility of the policy framework, particularly when FX intervention is frequent and disorderly market conditions are absent (IMF 2015e). As an analytical exercise, the impact of adding FX intervention to a standard Taylor-rule type of monetary policy function in a modified Forecasting and Policy Analysis (FPAS) model is explored in Ding and Peiris (forthcoming).