Generalized value at risk forecasting |
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Authors: | Aerambamoorthy Thavaneswaran Alex Paseka Julieta Frank |
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Institution: | 1. Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada;2. Aerambamoorthy.Thavaneswaran@umanitoba.ca;4. Department of Accounting and Finance, University of Manitoba, Winnipeg, Manitoba, Canada;5. Department of Agribusiness and Agricultural Economics, University of Manitoba, Winnipeg, Manitoba, Canada |
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Abstract: | AbstractIn this paper, using estimating function approach, a new optimal volatility estimator is introduced and based on the recursive form of the estimator a data-driven generalized EWMA model for value at risk (VaR) forecast is proposed. An appropriate data-driven model for volatility is identified by the relationship between absolute deviation and standard deviation for symmetric distributions with finite variance. It is shown that the asymptotic variance of the proposed volatility estimator is smaller than that of conventional estimators and is more appropriate for financial data with larger kurtosis. For IBM, Microsoft, Apple stocks and SP 500 index the proposed method is used to identify the model, estimate the volatility, and obtain minimum mean square error(MMSE) forecasts of VaR. |
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Keywords: | Estimating functions VaR forecasts data-driven models |
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