Stock Index Volatility Forecasting with High Frequency Data

Volatility Forecasting with High Frequency Data

The daily volatility is typically unobserved but can be estimated using high frequent tick-by-tick data. In this paper, we study the problem of forecasting the unobserved volatility using past values of measured volatility. Specifically, we use daily estimates of volatility based on high frequency data, called realized variance, and construct the optimal linear forecast of future volatility. Ut...

Forecasting Volatility using High Frequency Data

A Fuzzy Random Walk Technique to Forecasting Volatility of Iran Stock Exchange Index

Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange mark...

Combining high frequency data with non-linear models for forecasting energy market volatility

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two wa...

Do macrofactors help forecasting stock market volatility?∗

This study examines several dynamic heteroskedastic factor model specifications to test for the confidence set of model parametrizations that best incorporate economy-wide information for forecasting stock market volatility. To this end, diffusion indices (i.e. factors) are distilled from two large sets of US excess stock returns and macroeconomic variables. Using 40 years of data, the main emp...