Cross-border exchanges and volatility forecasting

Forecasting Volatility

Volatility Forecasting and Interpolation

Forecasting volatility is important to financial asset pricing because a more accurate forecast will allow for a more accurate model to price financial assets. Currently the VIX is used as a measure of volatility in the market as a whole, but a major issue with this is that it is calculated based on manually traded options on the S&P 500. Another method of forecasting volatility is that of solv...

Volatility Forecasting II: Stochastic Volatility Models and Empirical Evidence

ln(σt) = α + φ(ln(σt−1)− α) + ηt so that ln(σt) is an AR(1) process, where φ is a parameter that represents how quickly volatility gets pulled toward its mean, α. If ηt is normally distributed with mean 0 and variance σ η, then ln(σt) is normally distributed, and σt therefore has a lognormal distribution. To get the unconditional mean and variance of ln(σt), E[ln(σt)] = α + φ(E[ln(σt−1)]− α) + ...

Forecasting Crude Oil Price Volatility

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

Forecasting realized volatility: a review

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...