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Foreign Exchange Intervention by the Bank of Japan: Bayesian Analysis Using a Bivariate Stochastic Volatility Model

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  • Michael Smith
  • Andrew Pitts
Abstract
A bivariate stochastic volatility model is employed to measure the effect of intervention by the Bank of Japan (BOJ) on daily returns and volume in the USD/YEN foreign exchange market. Missing observations are accounted for, and a data-based Wishart prior for the precision matrix of the errors to the transition equation that is in line with the likelihood is suggested. Empirical results suggest there is strong conditional heteroskedasticity in the mean-corrected volume measure, as well as contemporaneous correlation in the errors to both the observation and transition equations. A threshold model is used for the BOJ reaction function, which is estimated jointly with the bivariate stochastic volatility model via Markov chain Monte Carlo. This accounts for endogeneity between volatility in the market and the BOJ reaction function, something that has hindered much previous empirical analysis in the literature on central bank intervention. The empirical results suggest there was a shift in behavior by the BOJ, with a movement away from a policy of market stabilization and toward a role of support for domestic monetary policy objectives. Throughout, we observe “leaning against the wind” behavior, something that is a feature of most previous empirical analysis of central bank intervention. A comparison with a bivariate EGARCH model suggests that the bivariate stochastic volatility model produces estimates that better capture spikes in in-sample volatility. This is important in improving estimates of a central bank reaction function because it is at these periods of high daily volatility that central banks more frequently intervene.

Suggested Citation

  • Michael Smith & Andrew Pitts, 2006. "Foreign Exchange Intervention by the Bank of Japan: Bayesian Analysis Using a Bivariate Stochastic Volatility Model," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 425-451.
    • Handle: RePEc:taf:emetrv:v:25:y:2006:i:2-3:p:425-451
    DOI: 10.1080/07474930600712897
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    References listed on IDEAS

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    1. Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility," Microeconomics Working Papers 22058, East Asian Bureau of Economic Research.
    2. Kearns, Jonathan & Rigobon, Roberto, 2005. "Identifying the efficacy of central bank interventions: evidence from Australia and Japan," Journal of International Economics, Elsevier, vol. 66(1), pages 31-48, May.
    3. Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 145-175.
    4. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    5. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    6. Baillie, Richard T. & Osterberg, William P., 1997. "Why do central banks intervene?," Journal of International Money and Finance, Elsevier, vol. 16(6), pages 909-919, December.
    7. Borus Jungbacker & Siem Jan Koopman, 2006. "Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 385-408.
    8. Harvey, Andrew C & Shephard, Neil, 1996. "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 429-434, October.
    9. Suk-Joong Kim & Jeffrey Sheen, 2018. "The Determinants of Foreign Exchange Intervention by Central Banks: Evidence from Australia," World Scientific Book Chapters, in: Information Spillovers and Market Integration in International Finance Empirical Analyses, chapter 1, pages 3-41, World Scientific Publishing Co. Pte. Ltd..
    10. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    11. Almekinders, Geert J. & Eijffinger, Sylvester C. W., 1996. "A friction model of daily Bundesbank and Federal Reserve intervention," Journal of Banking & Finance, Elsevier, vol. 20(8), pages 1365-1380, September.
    12. Kalimipalli, Madhu & Susmel, Raul, 2004. "Regime-switching stochastic volatility and short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 309-329, June.
    13. Elerain, Ola & Chib, Siddhartha & Shephard, Neil, 2001. "Likelihood Inference for Discretely Observed Nonlinear Diffusions," Econometrica, Econometric Society, vol. 69(4), pages 959-993, July.
    14. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
    15. Kim, Suk-Joong & Kortian, Tro & Sheen, Jeffrey, 2000. "Central bank intervention and exchange rate volatility -- Australian evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(3-4), pages 381-405, December.
    16. David Chan & Robert Kohn & Chris Kirby, 2006. "Multivariate Stochastic Volatility Models with Correlated Errors," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 245-274.
    17. Aguilar, Javiera & Nydahl, Stefan, 2000. "Central bank intervention and exchange rates: the case of Sweden," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(3-4), pages 303-322, December.
    18. Frederick Wong, 2003. "Efficient estimation of covariance selection models," Biometrika, Biometrika Trust, vol. 90(4), pages 809-830, December.
    19. Kim, Chan-Wung & Park, Jinwoo, 1994. "Holiday Effects and Stock Returns: Further Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(1), pages 145-157, March.
    20. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
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    3. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
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