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Appendix I. Inputs for Implementation
The implementation of SyRIN requires as inputs PoDs and total assets of the entities and sectors under analysis. Our dataset includes PoDs at the individual entity level (banks and insurance companies) and sectors (mutual funds, pension funds, and hedge funds). This appendix describes the method employed to estimate the PoDs for each of those entities and sectors.
The authors are grateful for the useful comments and contributions from E. Biffis, A. Bouveret, J. Gieck, C. Goodhart, P. Hartmann, H. Huang, R. Kosowski, H.Q. Li, N. Liang, M. Lovaglio, and D. Schoemaker. Special thanks to Felipe Nierhoff for his invaluable research assistance.
Systemic risk is defined as “the risk of widespread disruption to the provision of financial services that is caused by an impairment of all or parts of the financial system, which can cause serious negative consequences for the real economy” (BIS and others 2016, IMF 2013).
Financial externalities that have the potential to amplify shocks up to the point of disrupting financial intermediation cause systemic risk. Such externalities may themselves be impelled by cyclical and structural vulnerabilities (Adrian and others 2014), which drive the interconected structures among diverse financial intermediaries and markets, and pave the way for financial contagion.
An alternative framework is proposed by Duffie (2011), who recommends incorporating the largest banks, the largest asset classes, and the largest counterparties into the monitoring of systemic risk.
For an overview of other methods considering interconnectedness and systemic risk measurement, please refer to Malik and Xu (2017). However, most methods usually focus on a few sectors, commonly the banking or the banking and insurance sectors.
This is highly relevant for macroprudential policy, which aims to contain risks across the financial system as a whole (BIS and others 2016). Since banks are usually the key providers of credit to the economy, macroprudential policy has typically applied its policy levers to the banking system. However, as activity can migrate into non-banks, macroprudential policy also needs to consider the systemic risk that can build up from activities outside the banking system and develop policy responses to contain such risk (FSB 2011 and IMF 2013).
Such density characterizes (i) information of the individual firm’s value distributions, in its marginal densities; and (ii) information of the function that describes the association across firm values (interconnectedness structure), in its copula function. In contrast to correlation, which only captures linear dependence, copula functions characterize linear and non-linear dependence structures embedded in multivariate densities.
These channels of contagion are also referred to as the direct exposure channel and the asset liquidation channel, and have been highlighted in reports by the FSB and the Office of Financial Research as the main transmitters of systemic risk across different sectors of financial systems. (FSB 2014).
The CIMDO methodology is based on the minimum cross-entropy approach (Kullback 1959). Under this approach, a posterior multivariate distribution—the CIMDO density— is recovered using an optimization procedure by which a prior density function is updated with empirical information via a set of constraints. In this implementation, the empirical estimates of the PoD of individual banks act as the constraints, and the derived CIMDO density is the posterior density that is the closest to the prior distribution and consistent with these constraints. This methodology and its advantages relative to other parametric multivariate densities are presented in detail in Segoviano (2006) and Segoviano and Espinoza (2017). CIMDO approach estimations are robust under the probability integral transformation criteria (Diebold and others 1998).
While in most cases PoDs are estimated with market-based information, when such information does not exist, PoDs can also be estimated with supervisory information. Section IV.A discusses these cases.
While the framework is reduced-form and cannot disentangle specific systemic risk amplification mechanisms, SyRIN can help to identify sectors that might be highly vulnerable to such mechanisms. Thus, it is a tool that can guide further analysis in specific vulnerable sectors, supporting efforts of authorities to define adequate policy responses.
The suspension of redemptions by several U.K. retail property funds in July 2016 highlights the risks of liquidity mismatches in certain open-ended funds. The temporary suspensions came after outflows accelerated following the U.K. referendum to leave the EU. The funds that suspended redemptions eventually reopened, but only after cutting valuations significantly and selling properties under adverse conditions.
For example, by end-2015, the number of assets under management of closed-end funds was less than 2 percent of the U.S. fund industry.
As the authors note: “... our findings show that mutual funds with high liquidity needs that were left with exposure to the now illiquid securitized bonds played a significant role in spreading the crisis from the securitized bond market to the seemingly unrelated corporate bond market.”
Some examples of regulation affecting mutual funds could be: (i) shares may be redeemable at any time (for open ended-funds); (ii) NAV needs to be calculated daily; (iii) investment policies must be disclosed; and (iv) the use of leverage is limited.
Trading strategies are typically dynamic, as compared to mutual funds, which usually deploy buy-and-hold strategies.
Illiquidity encompasses market liquidity and funding liquidity. Market (asset) liquidity refers to the ability of unwinding positions quickly with minimal price impact. Market liquidity is systemic, in that it may be reduced during a financial disturbance. Funding liquidity, on the other hand, is the ability of an investor to obtain cash to meet obligations. Funding liquidity is typically idiosyncratic to the firm.
Credit strategies, such as distressed and convertible bond arbitrage, lost 19 percent and 26 percent, respectively, while emerging markets HF lost 30 percent in 2008 (Le Sourd 2009). Per the authors, investors were “given a painful reminder that HF are exposed to a variety of risk factors, such as credit risk, liquidity risk, and several equity risk factors.”
Gates are measures to stop a specific amount of redemptions from a fund vehicle. Gates can take two forms: (i) at the fund-level and (ii) at the investor level.
Hedge funds are not directly regulated, but do need to report to prime brokers.
The fees charged by HF are dependent on performance and are, in general, higher than mutual funds. The fee structure gives hedge fund managers the incentive to make profit, but also encourages risk-taking.
Insurance is characterized by an inverted production model: insurance premiums are received upfront and used to build reserves (technical provisions) to meet future obligations (insurance benefits). The latter are typically long term in life insurance, and short term in non-life insurance, although there are lines of business (for example, professional liability) that are “long tail” due to the longer duration of the claims reporting/settlement process.
Large aggregate claim amounts resulting from event occurrences affecting several policies simultaneously (for example, pandemics, earthquakes) can deplete standard reserves and extra provisions (such as resilience reserves), as well as eat into the regulatory capital buffer, forcing an insurer to sell illiquid, long-term assets at a significant discount.
A policyholder can lapse by walking away from a contract (for example, a term assurance policy with no cash value) or surrender a policy by partially or fully withdrawing the policy cash value (exit or surrender value). In both cases, the insurer is exposed to losses resulting from lower business volume (for example, initial expenses, overheads, asset management charges). In the case of cash values, minimum guarantees offered on surrender benefits can be costly in a deflationary environment. An expansionary environment induces policyholders to surrender policies to take advantage of more advantageously priced policies or alternative investment opportunities. Waves of lapses or surrenders could lead to asset fire sales by insurers. The empirical evidence on such bank-run-like behavior is limited.
Solvency II, for example, considers group supervision as an essential tool to supplement and complement the supervision of individual companies, and provides a range of governance and reporting requirements to facilitate group supervision.
Broeders and others (2016), for example, document three types of herd behavior: (i) weak herding, whereby pension plans follow a similar rebalancing behavior; (ii) semi-strong herding, meaning that pension plans respond in the same way to exogenous shocks; and (iii) strong herding, whereby some plans intentionally replicate the strategy of other funds.
There is evidence that changes to the regulatory framework, as well as to accounting standards, have increasingly limited the risk-taking capacity of pension funds (Franzen 2010). Also, there is anecdotal evidence that the lessons learned from the losses experienced by pension funds during the financial crisis in 2008 have made pension funds increasingly less tolerant of losses, while also strengthening their risk management processes.
While this is likely the case in the aggregate, there is also evidence that some US public pension funds may have increased their risk taking over recent years. This may be related to the fact that US public funds face distinct regulations that link the rate at which they discount their liabilities to their expected return on assets. This contrasts with most other pension funds, which link the liability discount rate to the relative riskiness of their promised pension benefits (Andonov and others 2013).
See discussion paper by the Bank of England and the Procyclicality Working Group: “Procyclicality and structural trends in investment allocation by insurance companies and pension funds” (July 2014).
In their survey or systemic risk analytics, Bisias and others (2012) note that “relatively few of the studies in our sample deal directly with pension funds or insurance companies despite the fact that the recent crisis actively involved these institutions.”
See Egginton and others (2010), who document how contagion effects arose during the collapse of AIG.
They employ Principle Component Analysis to measure commonality in returns across institutions. Using Granger causality tests, the authors identify the direction of the relationships among institutions and show that during the global financial crisis, the number of interconnections between financial institutions soared, with banks and insurance companies being central to the transmission of shocks to other institutions.
In this paper, as an illustrative application, we implement the FSI, DiDe, and Vulnerability Index. The FSMD allows us to estimate additional indicators based on different conditional and joint probabilities.
The FSI is based on the conditional expectation of a default probability measure developed by Huang (1992), who shows that this measure can also be interpreted as a relative measure of banking linkage. When the FSI=1.0 in the limit, banking linkage is weak (asymptotic independence). As the value of the FSI increases, banking linkage increases (asymptotic dependence). For empirical applications, see Hartmann and others (2001).
We are assuming that the three entities are in fact a subset of the entire universe of entities in the financial system. VI(X) would tend to P(X) as we sum over joints vis-à-vis all entities in universe.
The ES represents the (average) extreme loss to the system that occurs with a probability of 1percent (or less).
We adopt the following convention for certain sectors: HY = High yield bond mutual fund, IG = Investment grade bond mutual fund, and Sov = Sovereign bond fund sector. Bond = (Sovereign + HY + IG) bond fund, unless otherwise stated.
Another point to consider is that an analysis that incorporated thousands of funds within a sector would likely become cumbersome.
Risk parameters of banks’ loan portfolios (loans’ probabilities of default, exposures, and loss-given default) are used to estimate banks’ loss distributions (PLD). Supervisory information is used to define thresholds of capital buffers that, if violated, would indicate a distress event; for example, supervisory intervention. PLDs and thresholds are then used to estimate the banks’ PoD; that is, the probability of violating the supervisory threshold.
‘This refers to the aggregate of Sov, HY and IG bond mutual fund sectors.
In addition to leveraged loans and emerging markets debt.
Most mutual funds in the less liquid fixed income markets offer the promise of daily liquidity to their investors, while ETFs offer continuous intraday liquidity given that they trade at exchanges.
Under section 23(e) of the Investment Company Act of 1940 Provisions: “Open-end funds may not suspend the right of redemption, and open-end funds may not postpone the payment of redemption proceeds for more than seven days following receipt of a redemption request.” Under exceptional circumstances, mutual funds may be allowed to suspend redemptions temporarily should (i) the disposal of securities by a mutual fund is not “reasonably practicable” or (ii) it is not reasonably practicable for such fund fairly to determine its NAV. In theory, there is a mechanism in which the SEC has the authority to authorize asset managers to suspend when facing large redemptions. See, for example, the case of the Third Avenue Focused Credit Fund, which announced the suspension of redemptions on December 9, 2015, and blocked future investor redemptions following a period of large losses and investor outflows. However, it remains to be seen how effective this mechanism would be on a large scale, as it has never been tested before in a period of significant distress across financial markets.
See Chapter 1 of the October 2014 and May 2015 IMF’s Global Financial Stability reports.
The SEC adopted a new liquidity risk management rules in October 2016 and the FSB published a series of policy recommendations to address structural vulnerabilities associated with asset management (including liquidity risk management) in January 2017.
Greater flexibility in redemption and dealing frequency under the European Union’s UCITS Directive is a step in the right direction. The directive allows funds to have redemption frequencies of up to twice a month, which may help minimize the risk of liquidity mismatches. However, only a small proportion of funds invested in illiquid assets, such as high yield bond funds, offer redemption terms at a lower than daily frequency under UCITS, which has been related, amongst other reasons, to the inability of fund distribution platforms to accommodate any other fund dealing pattern than daily.
There is a growing body of academic work warning about the risks related to the growth of passive investing. Wurgler (2011) argues that the increase in passive investment inhibits the ability of active managers to beat benchmarks and can also lead to greater risk of asset price bubbles followed by crashes as it may encourage trading activity that exacerbates those risks. Wermers and Yao (2010) find that stocks with “excessive” levels of passive fund ownership exhibit more long-term pricing anomalies as well as a larger price reversal following trades. Sullivan and Xiong (2012) also find that the growing popularity of passive investing contributes to higher systemic market risk.
See Bank of England and the Procyclicality Working Group, 2014.
We use five-year CDS spreads from CMA retrieved through Datastream (or Bloomberg).