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Bayesian Arbitrage Threshold Analysis

Author

Listed:
  • Forbes, Catherine S.
  • Kalb, Guyonne R. J.
  • Kofman, Paul

Abstract

A Bayesian estimation procedure is developed for estimating multiple regime (multiple threshold) vector autoregressive models appropriate for deviations from financial arbitrage relationships. This approach has clear advantages over classical stepwise threshold autoregressive analysis. Whereas classical procedures first have to identify thresholds and then perform piecewise autoregressions, we simultaneously estimate threshold and autoregression parameters. To illustrate the Bayesian procedure, we estimate a no-arbitrage band within which index futures arbitrage is not profitable despite (persistent) deviations from parity.

Suggested Citation

  • Forbes, Catherine S. & Kalb, Guyonne R. J. & Kofman, Paul, "undated". "Bayesian Arbitrage Threshold Analysis," Department of Econometrics and Business Statistics Working Papers 267925, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:ags:monebs:267925
    DOI: 10.22004/ag.econ.267925
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    2. Nicholas Taylor, 2007. "A New Econometric Model of Index Arbitrage," European Financial Management, European Financial Management Association, vol. 13(1), pages 159-183, January.
    3. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    4. Hu, Jin-Li & Lin, Cheng-Hsun, 2008. "Disaggregated energy consumption and GDP in Taiwan: A threshold co-integration analysis," Energy Economics, Elsevier, vol. 30(5), pages 2342-2358, September.
    5. Lee, Jaeram & Kang, Jangkoo & Ryu, Doojin, 2015. "Common deviation and regime-dependent dynamics in the index derivatives markets," Pacific-Basin Finance Journal, Elsevier, vol. 33(C), pages 1-22.
    6. Kristyna Ters & Jörg Urban, 2018. "Estimating unknown arbitrage costs: evidence from a three-regime threshold vector error correction model," BIS Working Papers 689, Bank for International Settlements.
    7. Bajo-Rubio, Oscar & Diaz-Roldan, Carmen & Esteve, Vicente, 2006. "Is the budget deficit sustainable when fiscal policy is non-linear? The case of Spain," Journal of Macroeconomics, Elsevier, vol. 28(3), pages 596-608, September.
    8. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    9. Byeongseon Seo, 2004. "Testing for Nonlinear Adjustment in Smooth Transition Vector Error Correction Models," Econometric Society 2004 Far Eastern Meetings 749, Econometric Society.
    10. Liu, Xialu & Chen, Rong, 2020. "Threshold factor models for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 216(1), pages 53-70.
    11. Greb, Friederike & Krivobokova, Tatyana & von Cramon-Taubadel, Stephan & Munk, Axel, 2011. "On threshold estimation in threshold vector error correction models," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114599, European Association of Agricultural Economists.
    12. Emmanouil Mavrakis & Christos Alexakis, 2018. "Statistical Arbitrage Strategies under Different Market Conditions: The Case of the Greek Banking Sector," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2), pages 159-185, August.
    13. Kim, Bong-Han & Chun, Sun-Eae & Min, Hong-Ghi, 2010. "Nonlinear dynamics in arbitrage of the S&P 500 index and futures: A threshold error-correction model," Economic Modelling, Elsevier, vol. 27(2), pages 566-573, March.
    14. Goldman Elena & Nam Jouahn & Tsurumi Hiroki & Wang Jun, 2013. "Regimes and long memory in realized volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 521-549, December.
    15. Alexakis, Christos, 2010. "Long-run relations among equity indices under different market conditions: Implications on the implementation of statistical arbitrage strategies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(4), pages 389-403, October.
    16. Jaeram Lee & Doojin Ryu, 2016. "Asymmetric Mispricing and Regime-dependent Dynamics in Futures and Options Markets," Asian Economic Journal, East Asian Economic Association, vol. 30(1), pages 47-65, March.
    17. Tse, Yiuman, 2001. "Index arbitrage with heterogeneous investors: A smooth transition error correction analysis," Journal of Banking & Finance, Elsevier, vol. 25(10), pages 1829-1855, October.
    18. Ters, Kristyna & Urban, Jörg, 2020. "Estimating unknown arbitrage costs: Evidence from a 3-regime threshold vector error correction model," Journal of Financial Markets, Elsevier, vol. 47(C).
    19. Robles-Fernandez M. Dolores & Nieto Luisa & Fernandez M. Angeles, 2004. "Nonlinear Intraday Dynamics in Eurostoxx50 Index Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-28, December.
    20. Shively, Philip A., 2003. "The nonlinear dynamics of stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 505-517.

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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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