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Abstract

The basic methods and properties of filtered historical simulations are highlighted and compared to alternatives in the academic literature. Measure changes through changes in the parameters of the stochastic process are contrasted to the more commonly used changes in the distribution of residual returns.

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References

  • Audrino F., and G. Barone Adesi (2006), Average Conditional Correlations and Tree Structures for Multivariate GARCH Models, Journal of Forecasting, 25, pp. 579–600.

    Article  Google Scholar 

  • Barone Adesi G., K. Giannopoulos and L. Vosper (1999), VaR without Correlations for Portfolios of Derivative Securities, Journal of Futures Markets, August, pp. 583–602.

    Google Scholar 

  • Barone Adesi G., K. Giannopoulos and L. Vosper (2002), Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS), European Financial Management, March, pp. 31–58.

    Google Scholar 

  • Barone Adesi G., R. Engle and L. Mancini (2008), A GARCH Option Pricing Model with Filtered Historical Simulation, Review of Financial Studies, 21, May, pp. 1223–1258.

    Article  Google Scholar 

  • Black, F., and M. Scholes (1973), The Valuation of Options and Corporate Liabilities, Journal of Political Economy, 81, pp. 637–654.

    Article  Google Scholar 

  • Bollerslev, T., R. Y. Chou, and K. F. Kroner, 1992, ARCH Modeling in Finance: Review of the Theory and Empirical Evidence, Journal of Econometrics, 52, pp. 5–59.

    Article  Google Scholar 

  • Boudoukh, J., M. Richardson, and R. Whitelaw (1998), The Best of Both Worlds, RISK, May, pp. 64–67.

    Google Scholar 

  • Christoffersen, P., S. Heston, and K. Jacobs (2006), Option Valuation with Conditional Skewness, Journal of Econometrics, 131, pp. 253–284.

    Article  Google Scholar 

  • Efron, B. (1979), Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, 7 (1), pp. 1–26.

    Article  Google Scholar 

  • Girsanov, I. V. (1960), On Transforming a Certain Class of Stochastic Processes by Absolutely Continuous Substitution of Measures, Theory of Probability and Its Applications, 5, pp. 285–301.

    Article  Google Scholar 

  • Guégan, D., C. Chorro, and F. Ielpo (2010), Option Pricing for GARCH-Type Models with Generalized Hyperbolic Innovations, Centre d’Economie de la Sorbonne, Working Paper No. 23.

    Google Scholar 

  • Heston, S., and S. Nandi (2000), A Closed-Form GARCH Option Valuation Model, Review of Financial Studies, 13, pp. 585–625.

    Article  Google Scholar 

  • Hull, J. C. and A. White (1998), Incorporating Volatility Updating into the Historical Simulation Method for Value at Risk, Journal of Risk, 1 (1), pp. 5–19.

    Google Scholar 

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© 2014 Giovanni Barone Adesi

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Adesi, G.B. (2014). Introduction: Simulating Security Returns. In: Adesi, G.B. (eds) Simulating Security Returns: A Filtered Historical Simulation Approach. Palgrave Pivot, New York. https://doi.org/10.1057/9781137465559_1

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