Abstract
In this paper I present a model to forecast the daily Value at Risk (VaR) of the Peruvian stock market (measured through the general index of the Lima Stock Exchange: the IGBVL) based on intraday (high-frequency) data. Daily volatility is estimated using realised volatility and I adopted a regression quantile approach to calculate one-step predicted VaR values. The results suggest that the realised volatility is a useful measure to explain the Peruvian stock market volatility and I obtained sound results using quantile regression for risk estimation.
Highlights
Volatility has a vital role in risk management of financial assets such as stocks, indexes, currencies, etc
Since the 1980’s, many models have been proposed to estimate daily volatility based on daily data, following the approaches of Engle (1982) using ARCH-type models, and Taylor (1986) through stochastic variance models
Some topics of investigation are: modifications of the original measure of realised volatility in order to account for market microstructure effects; the study of statistical properties of realised volatility; modelling and forecasting realised volatility; and estimation of the Value at Risk (VaR) and daily return density forecast using realised volatility
Summary
Volatility has a vital role in risk management of financial assets such as stocks, indexes, currencies, etc. Some topics of investigation are: modifications of the original measure of realised volatility (as the sum of squared intraday returns) in order to account for market microstructure effects (bid-ask bounces, price discreteness or non-synchronous trading); the study of statistical properties of realised volatility; modelling and forecasting realised volatility; and estimation of the Value at Risk (VaR) and daily return density forecast using realised volatility. Useful reviews of these subjects can be found in McAleer and Medeiros (2008) and Liu et al (2015).
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