Using CAViaR models with implied volatility for value-at-risk estimation

Jooyong Jeon, James W. Taylor

Research output: Contribution to journalArticle

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Abstract

This paper proposes value-at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast-combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models—a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns
LanguageEnglish
Pages62-74
Number of pages13
JournalJournal of Forecasting
Volume32
Issue number1
Early online date27 Oct 2011
DOIs
StatusPublished - Jan 2013

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Implied Volatility
Value at Risk
Time series
Quantile Regression
Empirical Distribution
Appeal
Time Series Models
Conditional Distribution
Quantile
Model
Forecast
Standard Model
Tail
Synthesis
Implied volatility
Value at risk

Cite this

Using CAViaR models with implied volatility for value-at-risk estimation. / Jeon, Jooyong; Taylor, James W.

In: Journal of Forecasting, Vol. 32, No. 1, 01.2013, p. 62-74.

Research output: Contribution to journalArticle

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