Index-exciting CAViaR: A new empirical time-varying risk model

Dashan Huang; Baimin Yu; Zudi Lu; Frank J. Fabozzi; Sergio Focardi; Masao Fukushima

doi:10.2202/1558-3708.1805

Index-exciting CAViaR: A new empirical time-varying risk model

Dashan Huang
, Baimin Yu
, Zudi Lu
, Frank J. Fabozzi
, Sergio Focardi
, Masao Fukushima

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

10 Citations (Scopus)

Abstract

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. Copyright © 2010 The Berkeley Electronic Press. All rights reserved.

Original language	English
Article number	1
Journal	Studies in Nonlinear Dynamics and Econometrics
Volume	14
Issue number	2
DOIs	https://doi.org/10.2202/1558-3708.1805
Publication status	Published - Mar 2010
Externally published	Yes

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

CAViaR
Index-exciting CAViaR
Quantile regression
Time-varying model
VaR

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title = "Index-exciting CAViaR: A new empirical time-varying risk model",

abstract = "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. Copyright {\textcopyright} 2010 The Berkeley Electronic Press. All rights reserved.",

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author = "Dashan Huang and Baimin Yu and Zudi Lu and Fabozzi, \{Frank J.\} and Sergio Focardi and Masao Fukushima",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].",

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AU - Fabozzi, Frank J.

AU - Focardi, Sergio

AU - Fukushima, Masao

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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. Copyright © 2010 The Berkeley Electronic Press. All rights reserved.

AB - 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. Copyright © 2010 The Berkeley Electronic Press. All rights reserved.

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KW - Quantile regression

KW - Time-varying model

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ER -

Index-exciting CAViaR: A new empirical time-varying risk model

Abstract

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Research Keywords

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