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|a In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) or can be modelled globally by a stationary process 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.
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In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) or can be modelled globally by a stationary process 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. |
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Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden |
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Robust risk management accounting for nonstationarity and heavy tails Ying Chen; Vladimir Spokoiny, Berlin WIAS 2007, Online-Ressource (45 S., 522 KB) graph. Darst., Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Preprint / Weierstraß-Institut für Angewandte Analysis und Stochastik 1207, Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden. - Auch als gedr. Ausg. vorhanden, In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) or can be modelled globally by a stationary process 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., Systemvoraussetzungen: Acrobat reader., Archivierung/Langzeitarchivierung gewährleistet pdager DE-89, Forschungsbericht (DE-588)4155043-2 (DE-627)10467444X (DE-576)209815833 gnd-content, Chen, Ying oth, Spokoiny, Vladimir oth, Weierstraß-Institut für Angewandte Analysis und Stochastik Preprint 1207 1207 (DE-627)502494522 (DE-576)281291381 (DE-600)2209274-2 2198-5855, http://webdoc.sub.gwdg.de/ebook/serien/e/wias/2007/wias_preprints_1207.pdf Verlag Volltext, https://edocs.tib.eu/files/e01fn11/528712918.pdf application/pdf Verlag kostenfrei Volltext, DE-Zi4 epn:4127187417 del:202308180132, https://edocs.tib.eu/files/e01fn11/528712918.pdf LFER, LFER epn:3472048840 2019-05-07T00:00:00Z |
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Robust risk management: accounting for nonstationarity and heavy tails, Weierstraß-Institut für Angewandte Analysis und Stochastik, Preprint, 1207, In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) or can be modelled globally by a stationary process 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., Forschungsbericht |
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Robust risk management: accounting for nonstationarity and heavy tails |
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Robust risk management accounting for nonstationarity and heavy tails |
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Robust risk management accounting for nonstationarity and heavy tails Ying Chen; Vladimir Spokoiny |
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Robust risk management accounting for nonstationarity and heavy tails Ying Chen; Vladimir Spokoiny |
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Robust risk management accounting for nonstationarity and heavy tails Ying Chen; Vladimir Spokoiny |
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1207. Robust risk management: accounting for nonstationarity and heavy tails (2007) |
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Robust risk management |
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robust risk management accounting for nonstationarity and heavy tails |
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accounting for nonstationarity and heavy tails |
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Robust risk management: accounting for nonstationarity and heavy tails |
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