Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model

• Main Authors: Dashan HUANG, YU, Baimin, LU, Zudi, FOCARDI, Sergio, FABOZZI, Frank, FUKUSHIMA, Masao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: CAViaR; Index-exciting CAViaR; Quantile regression; Time-varying model; VaR; Finance and Financial Management; Management Sciences and Quantitative Methods
• 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
• Institution: Singapore Management University

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.