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Robust Risk Management. Accounting for Nonstationarity and Heavy Tails

Author

Listed:
  • Ying Chen
  • Vladimir Spokoiny

Abstract

In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) and 2) having conditionally normal distribution given the past. These two assumptions have been widely-used in many methods such as the RiskMetrics, one risk management method considered as industry standard. However these assumptions are unrealistic. The primary aim of the paper is to account for nonstationarity and heavy tails in time series by presenting a local exponential smoothing approach, by which the smoothing parameter is adaptively selected at every time point and the heavy-tailedness of the process is considered. A complete theory addresses both issues. In our study, we demonstrate the implementation of the proposed method in volatility estimation and risk management given simulated and real data. Numerical results show the proposed method delivers accurate and sensitive estimates.

Suggested Citation

  • Ying Chen & Vladimir Spokoiny, 2007. "Robust Risk Management. Accounting for Nonstationarity and Heavy Tails," SFB 649 Discussion Papers SFB649DP2007-002, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2007-002
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2007-002.pdf
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    Cited by:

    1. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.

    More about this item

    Keywords

    Exponential Smoothing; Spatial Aggregation.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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