Volatility Forecasting in Markets

Discover simple investment principles that help create financial stability and long-term wealth through disciplined decision-making.

This episode breaks down the overlooked costs that quietly reduce profits and explains how businesses can improve efficiency without sacrificing growth.

This article explores realized volatility—volatility measured from intraday returns—and the Heterogeneous Autoregressive (HAR) model. It explains why HAR-RV outperforms GARCH for short-term forecasts, the role of jumps and leverage effects, and how traders use 5-minute data to predict tomorrow's risk.

This article explains the GARCH framework, which models volatility as a function of past squared returns and past volatilities. It covers volatility clustering, mean reversion, and the key differences between ARCH, GARCH, and EGARCH. Applications include value-at-risk (VaR), option pricing, and dynamic hedging.