Back Matter

Back Matter

Author(s):
Emre Alper, Wenjie Chen, Jemma Dridi, Herve Joly, and Fan Yang
Published Date:
January 2017
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    Appendix I. A Snapshot of Financial Markets in the EAC

    Based on market capitalization, turnover ratio, number of listed companies and number of cross-listed companies, the Nairobi Stock Exchange remains by far the most developed equity market in the region, despite the development of the Dar es Salaam Stock Exchange and Uganda Securities Exchange in the past few years (Table A1.1). Government domestic debt markets are also more liquid and diversified in Kenya. Kenya and Uganda show a more diversified government domestic debt holder profile, with the banking sector holding about half of the domestic debt; in Tanzania, Rwanda, and Burundi, the share of the banking sector is in excess of 70 percent of the domestic debt (Tables A1.23).

    Table A1.1.Stock Market in Kenya, Rwanda, Tanzania, and Uganda(As of March 30, 2016)
    KenyaRwandaTanzaniaUganda
    Market capitalization (US$ billion)20.43.79.87.3
    Turnover rate8.51n.a2.950.19
    Number of listed companies9362118
    Numberof companies with RCL 159

    Regional cross listing on another EAC stock exchange.

    Sources: Stock exchange websites.
    Table A1.2.Holders of Government Domestic Debt in the EAC(As of end-2015)
    BurundiKenyaRwandaTanzaniaUganda
    (percent of total domestic debt)
    Banking institutions87.854.876.269.746.0
    Pension funds25.716.137.0
    Insurance companies8.67.92.0
    Others12.210.923.86.315.0
    Sources: EAC central banks and finance ministries.
    Table A1.3.Domestic Debt in Burundi, Kenya, Rwanda, Tanzania, and Uganda(As of end-2015)
    BurundiKenyaRwandaTanzaniaUganda
    Domestic Debt (US$ millions)67514,9705264,3183256.0
    T-Bill (US$ millions)13,8212741,3471,013
    Bonds (US$ millions)10,4851522,7812,243
    Domestic Debt (percent of GDP)23.424.46.49.613.2
    T-Bill (%of GDP)6.23.33.04.1
    Bonds (%of GDP)17.11.86.29.1
    Sources: EAC Central banks; and finance ministries.Note: Treasury bill.
    Appendix II. Detailed Information on Migration and Remittance Flow
    Table A2.1.EAC Migration Matrix, 2010 and 2013
    Source countryBurundiKenyaRwandaTanzaniaUgandaTotalSource countryBurundiKenyaRwandaTanzaniaUgandaTotal
    Destination countryDestination country
    Burundi033,5407,609041,148Burundi91657,10724,85179183,665
    Kenya0092,527288,455380,982Kenya2,2543,52733,015271,149309,945
    Rwanda44,785006,03720,73771,559Rwanda64,1981,43945,373106,501217,511
    Tanzania151,31391,14649,53630,110322,103Tanzania233,60659,23624,94718,524336,313
    Uganda101,82641,065123,86071,833338,584Uganda19,19044,35970,41125,093159,053
    Australia013,0343153,1532,31218,815Australia1,97116,0006313,9843,03025,616
    Belgium4,99183611,49838736918,081Belgium2,65503,805006,460
    France1,3701,0641,2796544194,786France1,8211,6956,02961253410,691
    Italy901,2921,7709752,0386,164Italy8123,2456611,3208916,929
    Germany7407,6471,2791,54649411,705Germany7159,5079761,3721,94314,513
    Sweeden5151,763210122,46024,949Sweeden2,8053,2635011,6403,62811,837
    Canada4,86026,1644,00523,00912,81170,848Canada5,23226,6574,33822,90012,93372,060
    The Netherlands2,6982,2531,1201,4491,0128,532The Netherlands2,5472,8081,1631,5231,2929,333
    United Kingdom2,678152,9993,14334,34754,122247,290United Kingdom0145,403031,10864,223240,734
    United States1,18785,1232,6148,85622,460120,240United States456112,60482518,92419,453152,262
    Other39,37432,91729,47364,779171,046337,589Other40,53548,367170,90338,37177,242375,418
    Total356,427457,303263,641317,160628,8452,023,376Total378,797475,499345,824250,086582,1342,032,340
    Sources: World Bank, Migration and Remittances Factbook, 2011 and 2014.
    Table A2.2.Remittances per Migrant, 2010 and 2013(U.S. dollars)
    Remittance receivingBurundiKenyaRwandaTanzaniaUgandaTotalRemittance receivingBurundiKenyaRwandaTanzaniaUgandaTotal
    Remitances sendingRemitances sending
    Burundi292114259Burundi14614221461390361
    Kenya1291,4421,123Kenya119468155355333
    Rwanda73114730267Rwanda10614611461412763
    Tanzania76711307756355Tanzania11114614451477449
    Uganda74711306165256Uganda1051461422146630
    Australia1,816002,2291,532Australia5073313158550242902733
    Belgium3421,8671,04602,298893Belgium3771314929
    France01,7802,84202,1811,346France5490116116340842
    Italy02,1958680505876Italy030821513001588
    Germany01,9939573299,4541,849Germany03366102572902343
    Sweeden01,90100311414Sweeden3573371399261044102619
    Canada3411,86203722,2911,246Canada3823489138339343302304
    The Netherlands5411,92399102,3741,091The Netherlands39335686065746441071
    United Kingdom3261,796919842,2221,625United Kingdom313635439863003
    United States02,0871,0713162,5622,003United States03774121242349353481
    Other1651,144507229497471Other161210445916642971398
    Total971,5003911741,221814Total128281349223818041312
    Sources: World Bank, Migration and Remittances Factbook, 2011 and 2014; and IMF staff calculations.
    Table A2.3.EAC: Remittances by Country of Origin, 2010 and 2013(Millions of U.S. dollars)
    EAC-Remittances by Country of Origin, 2010EAC-Remittances by Country of Origin, 2013
    Remittance receivingBurundiKenyaRwandaTanzaniaUgandaTotalRemittance receivingBurundiKenyaRwandaTanzaniaUgandaTotal
    Remitances sendingBurundiKenyaRwandaTanzaniaUgandaTotalRemitances sending
    Burundi0101011Burundi1244130
    Kenya0012416428Kenya02596103
    Rwanda3011519Rwanda727150166
    Tanzania12651523114Tanzania26871127151
    Uganda829381287Uganda265304100
    Australia02400529Australia153121370
    Belgium22120116Belgium105006
    France024016France107008
    Italy032015Italy01010011
    Germany01511522Germany03211034
    Sweeden0300710Sweeden111511634
    Canada249092988Canada2936956166
    The Netherlands141029The Netherlands1111610
    United Kingdom127533120402United Kingdom0456011256723
    United States01783358241United States04251896530
    Other638151585159Other7102757332523
    Total34686103557681,646Total491,338170591,0502,666
    Sources: World Bank, Migration and Remittances Factbook, 2011 and 2014.
    Table A2.4Intra-EAC Remittances by Country of Origin, 2010 and 2013(Millions of U.S. dollars)
    Intra-EAC Remittances by Country of Orgin, 2010Intra-EAC Remittances by Country of Orgin, 2010
    Remittance receivingBurundiKenyaRwandaTanzaniaUgandaTotalRemittance receivingBurundiKenyaRwandaTanzaniaUgandaTotal
    Remitances sendingRemitances sending
    Burundi0101011Burundi1244130
    Kenya0012416428Kenya02596103
    Rwanda3011519Rwanda727150166
    Tanzania12651523114Tanzania26871127151
    Uganda829381287Uganda265304100
    Total22946325454656Total351556719275551
    Sources: World Bank, Migration and Remittances Factbook, 2011 and 2014.
    Table A2.5.Total Population in EAC Countries, 2000–14(Millions)
    20102011201220132014
    Burundi8.48.68.89.09.2
    Kenya38.539.540.741.843.0
    Rwanda10.010.210.510.711.0
    Tanzania42.843.944.945.846.7
    Uganda34.035.136.337.638.7
    EAC133.7137.2141.2145.0148.7
    Source: IMF, World Economic Outlook, October 2016.
    Appendix III. Capital Account Restrictions: Country-Specific Details

    Kenya

    • Equities: In terms of residency, EAC investors are treated as local investors. There are no restrictions on purchases; however, issuances abroad by residents require Capital Market Authority (CMA) approval. For nonresidents, there is a regulation to limit foreign investors’ share up to 60 percent of the share of a capital of a listed company. Local issuance of securities by nonresidents requires CMA approval in accordance with the Capital Markets Act.

    • Money Market Instruments: No controls apply to purchases. However, both residents and nonresidents are subject to Central Bank Kenya approval for issuance of money market instruments (abroad for the former, locally for the latter).

    • Bonds: No controls apply to purchases. The issuance of bonds abroad by residents requires CMA approval. For nonresident sales, regulations governing securities of a participating nature apply.

    Tanzania

    • Equities: Purchases of shares on the DSE by a foreign investor are subject to a limit of 60 percent of total securities issued. Individual investors may not acquire more than 1 percent of an issue, and institutional investors may not acquire more than 10 percent. Investments must be made from securities accounts with local banks to be transferable. A three-month holding period also applies. Foreign companies from prescribed territories (that is, companies from the EAC—Burundi, Kenya, Rwanda, Uganda) may issue securities to the public and be cross-listed at an approved stock exchange in Tanzania, subject to approval by the Capital Markets and Securities Authority (CMSA).

    • Money Market Instruments: Nonresidents (outside EAC) may not hold government securities. Currently, no money market instruments are available. Purchases by residents are allowed only if funded fully by external sources. However, this requirement does not apply if the purchase is within the EAC, and it must be reported to the Bank of Tanzania for statistical purposes.

    • Bonds: Nonresidents (outside the EAC) are not permitted to hold government securities. Purchases of bonds on the DSE by a foreign investor are subject to a limit of 60 percent of total securities issued. Individual investors may not acquire more than 1 percent of an issue, and institutional investors may not acquire more than 10 percent. Investments must be made from securities accounts with local banks to be transferable. A three-month holding period also applies. Purchases and redemption of corporate debt securities must be in local currency. Bond purchases by residents are allowed only if funded fully by external sources and must be reported to the Bank of Tanzania for statistical purposes. Sales of bonds abroad by residents are subject to CMSA approval.

    Uganda

    • Equities: No restrictions

    • Money Market Instruments: No restrictions.

    • Bonds: No restrictions.

    South Africa

    • Equities: Approval is required for local purchases by nonresidents. Servicing must be from foreign sources if the funds are used abroad, and from domestic sources if the funds are transferred to South Africa. Effective February 27, 2014, unlisted technology, media, telecommunications, exploration, and other research and development companies may apply to the Financial Surveillance (FinSurv) department of the South African Reserve Bank for approval for primary listing abroad or to raise loans abroad and operating capital. Effective, February 27, 2014, companies listed on the Johannesburg Stock Exchange (JSE) may have a secondary listing and/or list depository receipt programs on foreign exchanges to facilitate local and FDI expansion. Approval is required for foreign entities to list shares and securities on the JSE Limited. The funds raised by the issuer are freely transferable. All inward-listed shares on the JSE Limited traded and settled in rand are classified as domestic for the purpose of trading on the exchange and are included on the JSE indices; institutional investors may invest in such shares without affecting their foreign exposure limits. Effective April 1, 2015, purchases by resident individuals are allowed within the foreign capital allowance limit of R11mil. for each individual in a calendar year (R1mil. single discretionary allowance plus R10mil. foreign capital allowance) or from the proceeds of any authorized foreign asset. Approval is required for issuances by residents abroad.

    • Money Market Instruments: There are no restrictions on local purchases by nonresidents. Approval is required for foreign entities to list money market instruments on the JSE Limited. The funds raised by the issuer are freely transferable. Money market instrument purchases abroad by residents are allowed within the foreign capital allowance limit of R5mil. for each individual in a calendar year (R1mil. single discretionary allowance plus R4mil. foreign capital allowance) or from the proceeds of any authorized foreign asset. South African institutional investors may invest in rand-denominated instruments issued abroad and in instruments issued by South African corporations in the foreign market, subject to foreign portfolio investment allowances. Approval is also required for issuances abroad by residents. Servicing must be undertaken from foreign sources if the funds are used abroad, and from domestic sources if the funds are used in South Africa.

    • Bonds: Approval is required for nonresident investors to purchase bonds locally. Approval is required for foreign entities to list bonds or other debt instruments on the JSE Limited. The funds raised by the issuer are freely transferable. Similar to money market instruments, bond purchases abroad by residents are allowed within the foreign capital allowance limit of R5mil. (R1mil. single discretionary allowance plus R4mil. foreign capital allowance) a calendar year or from the proceeds of any authorized foreign asset. Approval is also required for residents to issue bonds abroad. Servicing must be from foreign sources if the funds are used abroad, and from domestic sources if the funds are transferred to South Africa. Effective February 27, 2014, unlisted technology, media, telecommunications, exploration, and other research and development companies may apply to FinSurv for approval for primary listing abroad or to raise loans abroad and operating capital. Effective, February 27, 2014, companies listed on the JSE Limited may have a secondary listing and/or list depository receipt programs on foreign exchanges to facilitate local and foreign direct investment expansion.

    Appendix IV. An Analysis of Covered Interest Parity Condition for the EAC

    Background

    The covered interest parity (CIP) condition is one of the most relied upon indicators of financial market integration and market efficiency. Many models in international finance and open economy macroeconomics assume the CIP condition and use it as a key building block. CIP states that the actions of foreign exchange market participants, when hedged against exchange rate risk using the forward rates, should equalize interest rates on any two assets that differ only in currency of denomination.1 The CIP condition holds under the following four assumptions: (1) negligible transaction costs, (2) perfect capital mobility, (3) many participants in the spot and forward exchange markets with ample funds and no counterparty risk, and (4) underlying assets having identical political and default risk as well as liquidity, maturity, and seniority.

    There has been considerable interest in investigations of whether CIP holds for a variety of currency pairs and asset types.2 Empirical tests mainly consider larger currencies and support the validity of the CIP condition with the exception of periods characterized by financial turbulence when counterparty risk is especially prevalent.

    Algebraically, suppose that it,kd and it,kf and denote the interest rates on domestic currency and foreign-currency-denominated assets at time t, respectively, for an investment horizon of k. Suppose also that St denotes the spot exchange rate at time t—that is, the current price of one unit of foreign currency in domestic currency units—while Ft,k is the k-period forward exchange rate at time t—that is, the exchange rate currently agreed upon for a transaction k periods ahead. The CIP condition can be expressed as

    The actual k-period forward rate Ft,k may be different than the implied forward rate FCIP (right hand side of the equation) when any of the four CIP assumptions above is violated. This study treats violation of any of these assumptions under the general title “financial market impediments” and checks to see whether there are differences among EAC countries, where such markets exist, and compare the magnitude of such deviations to South Africa, a regional comparator. To that end the time series properties of deviations of the actual forward rate from the CIP implied forward rate, Δ, for different investment horizons (k= three, six, and 12 months) vis-à-vis the U.S. dollar are analyzed:

    Data

    Daily spot and nondeliverable forward (NDF) market exchange rates per U.S. dollar from 2011 to 2015 for Kenya, Tanzania, and Uganda for the EAC and the sub-Saharan Africa comparator South Africa are from Bloomberg (Figure A4.1).

    Figure A4.1.Kenya, Tanzania, Uganda, and South Africa: Spot and NDF Exchange Rates (2011–15)

    (National currency per US$)

    Source: Bloomberg.

    Note: 3m, 6m, and 12m stand for three-month, six-month and twelve-month maturities, respectively

    The interest rate data used to calculate the implicit CIP in this study are three-, six-, and 12-month Treasury Bill (T-bill) auction yields for Kenya, Tanzania, and Uganda from the EAC as well as for South Africa for 2011 through 2015 and are from central bank websites and from Bloomberg. Daily T-bill data for the corresponding maturities for the United States are from Federal Reserve Economic Data. Daily secondary market T-bill rates are not available in any country in the sample. Hence, we rely on weekly auctions for Kenya, Tanzania, and South Africa, and biweekly auctions for Uganda; 364-day T-bill auctions were conducted once a month before March 2013 in Kenya, so the 12-month analysis for Kenya is restricted to the post-March 2013 period.

    To derive descriptive statistics on CIP deviations, weekly averages of the daily data of the spot and NDF market exchange rates are calculated for Kenya, Tanzania, and South Africa while biweekly averages are calculated for Uganda. The corresponding maturity average U.S. T-bill yields (weekly and biweekly) are also calculated using daily data. All T-bill yields are available in annualized terms, the three-month implied forward exchange rate by first converting 91 -day annualized T-bill yields to three-month yields. Similarly, six-month yields are calculated from 182-day annualized T-bill yields. For the 364 days, annualized yields on 12-month T-bill rates are used. The implied CIP and the NDF for three-month, six-month, and 12-month horizons for Kenya, Tanzania, Uganda, and South Africa are plotted in Figures A4.25. Tables A4.13 provide summary statistics for the CIP deviations and the absolute CIP deviations, respectively.

    Figure A4.2.Kenya: Actual (NDF) and Implied Forward Exchange Rates (2011–15)

    (National currency per US$)

    Sources: Central Bank of Kenya; Bloomberg; and IMF staff estimates.

    Figure A4.3.Tanzania: Actual (NDF) and Implied Forward Exchange Rates (2011–15)

    (National currency per US$)

    Sources: Bank of Tanzania; Bloomberg; U.S. Federal Reserve; and IMF staff estimates.

    Figure A4.4.Uganda: Actual (NDF) and Implied Forward Exchange Rates (2011–15)

    (National currency per US$)

    Sources: Bank of Uganda; Bloomberg; U.S. Federal Reserve; and IMF staff estimates.

    Figure A4.5.South Africa: Actual and Implied Forward Exchange Rates (2011–15)

    (National currency per US$)

    Sources: Reserve Bank of South Africa; Bloomberg; U.S. Federal Reserve; and staff estimates.

    Table A4.1.Summary Statistics of CIP Deviations Relative to the United States
    Three-month maturity
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    Period1/11-12/151/11-12/151/11-12/151/11-12/15
    No. of observations248122126257
    Median–0.340.03–0.050.04
    Mean–0.170.160.030.05
    Minimum–1.43–1.11–1.70–0.07
    Maximum1.842.853.490.31
    Standard deviation0.640.710.620.07
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    Six-month maturity
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    Period1/11-12/151/11-12/151/11-12/151/11-12/15
    No. of observations232121127256
    Median–1.00–0.60–0.850.02
    Mean–0.78–0.47–0.890.03
    Minimum–2.94–3.84–3.28–0.33
    Maximum2.833.865.540.39
    Standard deviation1.251.320.940.11
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    12-month maturity1
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    Period3/13-12/151/11-9/151/11-12/151/11-12/15
    No. of observations145117125254
    Median–3.24–2.00–2.74–0.09
    Mean–2.95–2.28–2.73–0.08
    Minimum–5.58–5.86–5.97–0.98
    Maximum–0.144.315.100.52
    Standard deviation1.191.991.420.27
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    Sources: Bloomberg; and IMF staff calculations.

    Twelve-month T-bill auctions were conducted once a month until March 2013 in Kenya. The descriptive statistics reported in the table for 12-month maturity are based on weekly auctions since March 2013. 12-month NDF rates for Tanzania are not available since September 2015.

    Table A4.2.Summary Statistics of Absolute CIP Deviations Relative to the United States
    Three-month maturity
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    Period1/11-12/151/11-12/151/11-12/151/11-12/15
    No. of observations248122126257
    Median0.490.450.260.05
    Mean0.550.560.410.06
    Minimum0.010.010.000.00
    Maximum1.842.853.490.31
    Standard deviation0.370.470.470.06
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    Six-month maturity
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    Period1/11-12/151/10-12/151/11-12/151/11-12/15
    No. of observations232121127256
    Median1.261.121.040.09
    Mean1.240.990.870.08
    Minimum0.010.020.040.00
    Maximum2.943.865.540.39
    Standard deviation0.770.840.770.07
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    12-month maturity1
    (Percent)
    KenyaTanzaniaUgandaSouth Africa
    No. of observations145117125254
    Median3.142.432.840.21
    Mean2.902.422.740.16
    Minimum0.140.120.590.00
    Maximum5.585.865.970.98
    Standard deviation1.271.421.200.18
    FrequencyWeeklyBiweeklyBiweeklyWeekly
    Sources: Bloomberg and IMF staff calculations.

    Twelve-month T-bill auctions were conducted once a month until March 2013 in Kenya. The descriptive statistics reported in the table for 12-month maturity are based on weekly auctions since March 2013. 12-month NDF rates for Tanzania are not available since September 2015.

    Table A4.3.Summary Statistics of Absolute CIP Deviations Relative to the EAC1
    Three-month maturity
    (Percent)
    KenyaTanzaniaUganda
    Period3/11-9/153/11-9/154/11-9/15
    No. of observations108108107
    Median0.930.673.24
    Mean1.000.783.07
    Minimum0.010.020.18
    Maximum3.063.656.86
    Standard deviation0.640.621.26
    FrequencyBiweeklyBiweeklyBiweekly
    Six-month maturity
    (Percent)
    KenyaTanzaniaUganda
    Period6/11-9/156/11-9/156/11-9/15
    No. of observations100100105
    Median0.931.281.27
    Mean1.021.361.49
    Minimum0.020.000.08
    Maximum3.954.695.68
    Standard deviation0.801.131.07
    FrequencyBiweeklyBiweeklyBiweekly
    12-month maturity2
    (Percent)
    KenyaTanzaniaUganda
    Period3/11-9/153/11-9/153/11-9/15
    No. of observations106106105
    Median1.712.002.06
    Mean2.652.552.74
    Minimum0.010.000.00
    Maximum10.419.3610.78
    Standard deviation2.352.102.41
    FrequencyBiweeklyBiweeklyBiweekly
    Sources: Bloomberg; World Economic Outlok database; and IMF staff calculations.

    For each country, generic EAC averages are calculated for each variable in the CIP, using purchasing power parity GDP weights of the remaining two countries.

    Twelve-month T-bill auctions were conducted once a month until March 2013 in kenya. The statistics reported in the table for 12-month maturity is based on weekly auctions since March 2013. 12-month NDF rates for Tanzania is not available since September 2015.

    Appendix V. Application of Convergence Concepts in the EAC

    Background

    The concept of financial market integration is closely related to the law of one price.1 Capital mobility, when not impeded, would ensure that financial market prices—yields—of assets identical in denomination, maturity, and risk would be the same. Thus, the presence of differences in (1) capital controls, (2) underlying risks, and (3) expectations of currency movements would cause a “wedge” among financial prices or implied returns. Such conditions in general do not hold.2 Nevertheless, it would still be informative to see the evolution of the wedge to see if such differences among countries move in the “right” direction—that is, if the dispersion of such yields is decreasing in time or the returns are converging. A reduction in dispersion, or “convergence” of yields of assets with the same maturity, would indicate increasing financial integration through reduction in capital controls and/or diminishing of differences in underlying risks, and expectations of currency depreciation/ appreciation for the duration of the holding period.

    Data and Methodology

    The preceding analysis introduces the concepts of β-convergence and σ-convergence in yields of selected assets and maturities within the EAC countries during 2011–15. Specifically, it considers weekly stock market returns in the EAC (with the exception of Burundi); weekly averages of overnight interbank rates; and weekly and biweekly three-, six-, and 12-month T-bill auction yields in domestic currencies.3 Data are from Bloomberg and EAC country central banks (Figure A5.1).

    Figure A5.1.Various EAC Financial Returns Data Used in Convergence Analysis

    Two different convergence concepts are used: β-convergence and σ-convergence, because they capture different aspects of financial integration. While the former shows to what extent financial convergence has been achieved within the EAC in the observed sample, the latter shows whether markets are moving toward integration over time.

    β-convergence enables the identification of the speed at which shocks are eliminated on the yields of assets with identical maturities traded in EAC countries’ financial markets. The measure involves estimating the following time series regression for each country or panel regression for the whole EAC:

    Spread variable denotes the spread of yield on an underlying asset of a specific maturity (one day or three, six, or 12 months) between country i and the synthetic EAC region, formed from the PPP-GDP weights of the remaining four EAC countries.4 The weights are Burundi, 2.3 percent; Kenya, 36.6 percent; Rwanda, 5.2 percent; Tanzania, 35.1 percent; and Uganda, 20.7 percent in 2013. Nrepresents the number of lags used in this regression to clean the residuals from autocorrelation. In our estimations we include lags up to eight weeks during 2011–15.

    The β0 coefficient would provide the average “wedge” between an underlying asset for a specific maturity (one day or three, six, or 12 months) between country i and the synthetic EAC region. The β1 coefficient, if significantly negative, would suggest the existence of convergence—that is, mean reversion taking place between country i and the synthetic EAC region for an underlying asset for a specific maturity (one day or three, six, or 12 months). The magnitude of the β1 coefficient would denote the speed of convergence, with speed increasing in β1. Panel- and country-specific time series regression results are given in Table A5.1.

    σ-convergence occurs when the dispersion of the levels of a yield of a specific asset between different EAC countries tends to decrease over time. The concept owes its origins to seminal work by Barro and Sala-i Martin (1992) on cross-sectional dispersion of income. The current analysis checks whether the dispersion, “σ,” tends to decrease over time for a number of selected assets in the EAC:

    Rit denotes the return for an asset in country i at time t, and R¯t denotes the EAC-wide mean return at time t. Lower σ values imply higher levels of financial convergence. In theory, full integration (“law of one price”) would be implied when the standard deviation is zero. Figure A5.2 gives the evolution of σ within EAC for a number of assets.

    Table A5.1.Pooled and Individual Beta Coefficients of EAC Countries’ Selected Asset Yields (2011–15)
    Stock Exchange Markets
    Pooled RegressionIndividual Regression
    OLSFixed effectsRandom effectsGMMKENRWATZAUGA
    β–0.747***–0.762***–0.747***–0.782***–0.781***–0.657***–0.742***–0.893***
    (0.000)(0.000)(0.00)(0.00)(0.000)(0.000)(0.000)(0.000)
    N676676676676169169169169
    R-squared0.3950.3990.3990.4190.3190.3860.470
    Half-life of deviations (weeks)0.50.50.50.50.50.60.50.3
    Overnight Rate Market
    Pooled RegressionIndividual Regression
    OLSFixed effectsRandom effectsGMMBDIKENRWATZAUGA
    β–0.074***–0.114***–0.074***–0.127***–0.059 **–0.311***–0.032–0.191***–0.136 **
    (0.000)(0.000)(0.000)(0.000)(0.026)(0.000)(0.136)(0.000)(0.011)
    N850850850850170170170170170
    R-squared0.0550.0570.0590.0380.2630.0190.1860.042
    Half-life of deviations (weeks)9.15.79.05.111.41.921.13.34.7
    Three-month T-Bill Market
    Pooled RegressionIndividual Regression
    OLSFixed effectsRandom effectsGMMBDIKENRWATZAUGA
    β–0.024***–0.042***–0.024***–0.036 **–0.058–0.122***–0.014–0.049***–0.019
    (0.000)(0.000)(0.000)(0.017)(0.075)(0.000)(0.132)(0.005)(0.131)
    N850850850850170170170170170
    R-squared0.0820.0810.0860.0220.4490.1830.2040.340
    Half-life of deviations (weeks)28.116.328.518.711.65.349.213.835.4
    Six-month T-Bill Market
    Pooled RegressionIndividual Regression
    OLSFixed effectsRandom effectsGMMBDIKENRWATZAUGA
    β–0.011–0.019–0.011–0.0050.038–0.065***–0.010–0.030***–0.015
    (0.069)(0.016)(0.069)(0.816)(0.249)(0.002)(0.480)(0.007)(0.101)
    N846846846846167170170170170
    R-squared0.0440.0470.0490.0150.3320.0670.3370.445
    Half-life of deviations (weeks)61.335.561.3133.4–18.710.368.223.045.7
    12-month T-Bill Market
    Pooled RegressionIndividual Regression
    OLSFixed effectsRandom effectsGMMBDIKENRWATZAUGA
    β–0.019–0.086***–0.019–0.143–0.084 **–0.255***–0.015–0.041–0.116***
    (0.035)(0.000)(0.034)(0.231)(0.043)(0.000)(0.649)(0.154)(0.000)
    N575575575575115115115115115
    R-squared0.2710.2310.2760.0260.3270.5250.1640.631
    Half-life of deviations (weeks)35.47.735.44.57.92.445.616.55.6
    Sources: Bloomberg EAC authorities; IMF staff estimates.Note: Standard errors in parentheses. *** denotes significance at 1 percent, ** at 5 percent, and * at 10 percent levels.

    Figure A5.2.Sigma-Convergence Analysis for the EAC Countries

    See for example, Alper and Ardic (2010).

    See Officer and Wilier. (1970) and Taylor (1992) for comprehensive surveys of this literature.

    In a monetary union, for example, conditions (1) and (3) would mostly be the same across member coun-tries but condition (2) may still be different.

    As discussed earlier, there are no formal onshore forward market rates in the EAC countries to show market expectations of depreciation/appreciation during the holding period of an asset. One option would be to analyze ex post returns in U.S. dollars based on actual changes in currencies’ values during the holding period, but that would bring additional noise in the data. Thus, identical rates of expected depreciation or appreciation among EAC countries were assumed.

    Yabara (2012) uses Kenya as the benchmark market. For each country, this paper uses as the benchmark country a synthetic EAC country (based on PPP GDP weights) after excluding the country itself. This methodology enables reporting results also for Kenya.

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