A Note on Forecasting Daily Peruvian Stock Market Volatility Risk Using Intraday Returns

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.

Keywords

• High frequency data
• Quantile Regression
• Value-at-Risk