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http://hdl.handle.net/2123/8170
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| Title: | Estimating Value At Risk |
| Authors: | Lu, Zudi Huang, Hai Gerlach, Richard Discipline of Business Analytics |
| Keywords: | Asymmetric Laplace distribution Exponentially weighted moving average (EWMA) forecasting Skewed EWMA Skewness and heavy tails Time-varying shape parameter Value-at-risk (VaR) |
| Issue Date: | Jan-2010 |
| Publisher: | Business Analytics. |
| Series/Report no.: | OMEWP 01/2010 |
| Abstract: | Significantly driven by JP Morgan's RiskMetrics system with EWMA (exponentially weighted moving average) forecasting technique, value-at-risk (VaR) has turned to be a popular measure of the degree of various risks in financial risk management. In this paper we propose a new approach termed skewed-EWMA to forecast the changing volatility and formulate an adaptively efficient procedure to estimate the VaR. Differently from the JP Morgan's standard-EWMA, which is derived from a Gaussian distribution, and the Guermat and Harris (2001)'s robust-EWMA, from a Laplace distribution, we motivate and derive our skewed-EWMA procedure from an asymmetric Laplace distribution, where both skewness and heavy tails in return distribution and the time-varying nature of them in practice are taken into account. An EWMA-based procedure that adaptively adjusts the shape parameter controlling the skewness and kurtosis in the distribution is suggested. Backtesting results show that our proposed skewed-EWMA method offers a viable improvement in forecasting VaR. |
| URI: | http://hdl.handle.net/2123/8170 |
| Department/Unit/Centre: | Discipline of Business Analytics |
| Type of Work: | Working Paper |
| Appears in Collections: | Working Papers - Business Analytics |
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