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Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...
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Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...
Persistent link: https://www.econbiz.de/10011299966
We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...
Persistent link: https://www.econbiz.de/10010529886
-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and … probability of the market crashes, beyond and above other option-implied variables. Stock-specific tail loss measure predicts … cares about the left tail of her wealth distribution benefits from using the tail loss measure as an information variable to …
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risk capital allocation with the average value-at-risk measure (AVaR). AVaR is the average loss provided that the loss is …
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