Research and publications
Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model
Instead of assuming the distribution of return series, Engle and Manganelli (2004) propose a new Value-at-Risk (VaR) modeling approach, Conditional Autoregressive Value-at-Risk (CAViaR), to directly compute the quantile of an individual asset’s returns which performs better in many cases than those that invert a return distribution. This paper explores more flexible CAViaR models that allow VaR prediction to depend upon a richer information set involving returns on an index. Specifically, we formulate a time-varying CAViaR model whose parameters vary according to the evolution of the index. A revisited version of this paper was published in the March 2010 issue of Studies in Nonlinear Dynamics & Econometrics.
Author(s):
Frank J. Fabozzi, Sergio Focardi, Masao Fukushima, Dashan Huang, Zudi Lu, Baimin Yu
Summary:
Instead of assuming the distribution of return series, Engle and Manganelli (2004) propose a new Value-at-Risk (VaR) modeling approach, Conditional Autoregressive Value-at-Risk (CAViaR), to directly compute the quantile of an individual asset’s returns which performs better in many cases than those that invert a return distribution. This paper explores more flexible CAViaR models that allow VaR prediction to depend upon a richer information set involving returns on an index. Specifically, we formulate a time-varying CAViaR model whose parameters vary according to the evolution of the index. A revisited version of this paper was published in the March 2010 issue of Studies in Nonlinear Dynamics & Econometrics.
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Register/Log in| Type : | Working paper |
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| Date : | 19/09/2008 |
| Keywords : |
Risk Management |
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