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Implicit Entropic Market Risk-Premium from Interest Rate Derivatives

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  • J. Arismendi-Zambrano

    (Department of Economics, Finance and Accounting, Maynooth University, Ireland & ICMA Centre, Henley Business School, University of Reading, Whiteknights, Reading, United Kingdom.)

  • R. Azevedo

    (Bradesco Asset Management - BRAM, Sao Paulo, Brazil)

Abstract

Implicit in interest rate derivatives are Arrow-Debreu prices (or state price densities, SPDs) that contain fundamental information for risk and portfolio management in interest rate markets. To extract such information from interest rate derivatives, we propose a nonparametric method to estimate state prices based on the minimization of the Cressie-Read (Entropic) family function between potential SPDs and the empirical probability measure. An empirical application of the method, in the US interest rates and derivatives market, shows that the entropic based risk-neutral density measure highlight potential risks previous to the 2007/2008 financial crisis, and the potential arbitrage burden during the Quantitative Easing period.

Suggested Citation

  • J. Arismendi-Zambrano & R. Azevedo, 2020. "Implicit Entropic Market Risk-Premium from Interest Rate Derivatives," Economics Department Working Paper Series n303-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    • Handle: RePEc:may:mayecw:n303-20.pdf
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    References listed on IDEAS

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    1. Barletta, Andrea & Santucci de Magistris, Paolo & Violante, Francesco, 2019. "A non-structural investigation of VIX risk neutral density," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 1-20.
    2. Diebold, Francis X. & Li, Canlin & Yue, Vivian Z., 2008. "Global yield curve dynamics and interactions: A dynamic Nelson-Siegel approach," Journal of Econometrics, Elsevier, vol. 146(2), pages 351-363, October.
    3. Ahmadi-Javid, Amir & Fallah-Tafti, Malihe, 2019. "Portfolio optimization with entropic value-at-risk," European Journal of Operational Research, Elsevier, vol. 279(1), pages 225-241.
    4. Bikbov, Ruslan & Chernov, Mikhail, 2013. "Monetary policy regimes and the term structure of interest rates," Journal of Econometrics, Elsevier, vol. 174(1), pages 27-43.
    5. Aharon Ben-Tal, 1985. "The Entropic Penalty Approach to Stochastic Programming," Mathematics of Operations Research, INFORMS, vol. 10(2), pages 263-279, May.
    6. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    7. Haitao Li & Feng Zhao, 2009. "Nonparametric Estimation of State-Price Densities Implicit in Interest Rate Cap Prices," Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4335-4376, November.
    8. Gzyl, Henryk & Mayoral, Silvia, 2008. "Determination of risk pricing measures from market prices of risk," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 437-443, December.
    9. Almeida, Caio & Graveline, Jeremy J. & Joslin, Scott, 2011. "Do interest rate options contain information about excess returns?," Journal of Econometrics, Elsevier, vol. 164(1), pages 35-44, September.
    10. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2006. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871525.
    11. David Heath & Robert Jarrow & Andrew Morton, 2008. "Bond Pricing And The Term Structure Of Interest Rates: A New Methodology For Contingent Claims Valuation," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 13, pages 277-305, World Scientific Publishing Co. Pte. Ltd..
    12. Fleischhacker, Adam J. & Fok, Pak-Wing, 2015. "On the relationship between entropy, demand uncertainty, and expected loss," European Journal of Operational Research, Elsevier, vol. 245(2), pages 623-628.
    13. Frank Fabozzi & Radu Tunaru & George Albota, 2009. "Estimating risk-neutral density with parametric models in interest rate markets," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 55-70.
    14. M. Ryan Haley & Todd B. Walker, 2010. "Alternative tilts for nonparametric option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(10), pages 983-1006, October.
    15. Stutzer, Michael, 1996. "A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-1652, December.
    16. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    17. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Cowles Foundation Discussion Papers 1569, Cowles Foundation for Research in Economics, Yale University.
    18. Leonidas S. Rompolis & Elias Tzavalis, 2010. "Risk Premium Effects On Implied Volatility Regressions," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(2), pages 125-151, June.
    19. Haitao Li & Feng Zhao, 2006. "Unspanned Stochastic Volatility: Evidence from Hedging Interest Rate Derivatives," Journal of Finance, American Finance Association, vol. 61(1), pages 341-378, February.
    20. Henryk Gzyl & Silvia Mayoral, 2012. "Determination of the Probability Distribution Measures from Market Option Prices Using the Method of Maximum Entropy in the Mean," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(4), pages 299-312, August.
    21. Almeida, Caio & Garcia, René, 2012. "Assessing misspecified asset pricing models with empirical likelihood estimators," Journal of Econometrics, Elsevier, vol. 170(2), pages 519-537.
    22. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Levine's Bibliography 321307000000000307, UCLA Department of Economics.
    23. Leiss, Matthias & Nax, Heinrich H., 2018. "Option-implied objective measures of market risk," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 241-249.
    24. Unal, Haluk & Madan, Dilip & Guntay, Levent, 2003. "Pricing the risk of recovery in default with absolute priority rule violation," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1001-1025, June.
    25. Chernov, Mikhail & Ghysels, Eric, 2000. "A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation," Journal of Financial Economics, Elsevier, vol. 56(3), pages 407-458, June.
    26. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    27. Shuiabi, Eyas & Thomson, Vince & Bhuiyan, Nadia, 2005. "Entropy as a measure of operational flexibility," European Journal of Operational Research, Elsevier, vol. 165(3), pages 696-707, September.
    28. Rompolis, Leonidas S. & Tzavalis, Elias, 2008. "Recovering Risk Neutral Densities from Option Prices: A New Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(4), pages 1037-1053, December.
    29. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    30. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    31. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2006. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692083.
    32. Brandtner, Mario & Kürsten, Wolfgang & Rischau, Robert, 2018. "Entropic risk measures and their comparative statics in portfolio selection: Coherence vs. convexity," European Journal of Operational Research, Elsevier, vol. 264(2), pages 707-716.
    33. Marco Frittelli, 2000. "The Minimal Entropy Martingale Measure and the Valuation Problem in Incomplete Markets," Mathematical Finance, Wiley Blackwell, vol. 10(1), pages 39-52, January.
    34. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    35. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    36. Bali, Turan G. & Murray, Scott, 2013. "Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1145-1171, August.
    37. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    38. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
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    More about this item

    Keywords

    Risk management; Risk analysis; Nonparametric Asset Pricing; State Price Density; Interest Rate Derivatives;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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