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Time Dependent Relative Risk Aversion

In: Risk Assessment

Author

Listed:
  • Enzo Giacomini

    (Humboldt-University of Berlin)

  • Michael Handel

    (Dr. Nagler & Company GmbH)

  • Wolfgang K. Härdle

    (Humboldt-University of Berlin)

Abstract

Risk management has developed in the recent decades to be one of the most fundamental issues in quantitative finance. Various models are being developed and applied by researchers as well as financial institutions. By modeling price fluctuations of assets in a portfolio, the loss can be estimated using statistical methods. Different measures of risk, such as standard deviation of returns or confidence interval Value at Risk, have been suggested. These measures are based on the probability distributions of assets' returns extracted from the data-generating process of the asset. However, an actual one dollar loss is not always valued in practice as a one dollar loss. Purely statistical estimation of loss has the disadvantage of ignoring the circumstances of the loss. Hence the notion of an investor's utility has been introduced. Arrow [2] and [10] were the first to introduce elementary securities to formalize economics of uncertainty. The so-called Arrow-Debreu securities are the starting point of all modern financial asset pricing theories. Arrow—Debreu securities entitle their holder to a payoff of 1$ in one specific state of the world, and 0 in all other states of the world. The price of such a security is determined by the market, on which it is tradable, and is subsequent to a supply and demand equilibrium. Moreover, these prices contain information about investors' preferences due to their dependence on the conditional probabilities of the state of the world at maturity and due to the imposition of market-clearing and general equilibrium conditions. The prices reflect investors' beliefs about the future, and the fact that they are priced differently in different states of the world implies, that a one-dollar gain is not always worth the same, in fact its value is exactly the price of the security.

Suggested Citation

  • Enzo Giacomini & Michael Handel & Wolfgang K. Härdle, 2009. "Time Dependent Relative Risk Aversion," Contributions to Economics, in: Georg Bol & Svetlozar T. Rachev & Reinhold Würth (ed.), Risk Assessment, pages 15-46, Springer.
  • Handle: RePEc:spr:conchp:978-3-7908-2050-8_3
    DOI: 10.1007/978-3-7908-2050-8_3
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    References listed on IDEAS

    as
    1. Ait-Sahalia, Yacine & Lo, Andrew W., 2000. "Nonparametric risk management and implied risk aversion," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 9-51.
    2. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    3. 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.
    4. K. J. Arrow, 1964. "The Role of Securities in the Optimal Allocation of Risk-bearing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 31(2), pages 91-96.
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    Citations

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    Cited by:

    1. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Yuri Golubev & Wolfgang Härdle & Roman Timonfeev, 2008. "Testing Monotonicity of Pricing Kernels," SFB 649 Discussion Papers SFB649DP2008-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Bedoui, Rihab & Hamdi, Haykel, 2015. "Option-implied risk aversion estimation," The Journal of Economic Asymmetries, Elsevier, vol. 12(2), pages 142-152.
    4. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.

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    More about this item

    Keywords

    Utility Function; Risk Aversion; Asset Price; Option Price; Implied Volatility;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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