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 t...
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| Main Authors: | , , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/4781 https://ink.library.smu.edu.sg/context/lkcsb_research/article/5780/viewcontent/HuangD_IndexExcitingCaviar_PubVer.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| 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. In this paper we explore 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. The empirical evidence reported in this paper suggests that our time-varying CAViaR models can do a better job for VaR prediction when there are spillover effects from one market or market segment to other markets or market segments. |
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