As I was building a summary page about the volatility of the NASDAQ-100 Index, as we looked at the Dow Jones Industrial Average and the S&P 500 Index in earlier posts, I thought that volatility data is actually a fairly scarce commodity on the internet. Due to the heteroskedasticity of financial markets, using dynamically forecast volatility is critical to investment decisions and to simple analysis, such as linear regressions, which should be variance weighted (making the common least-squares regression actually equivalent to the more general maximum likelihood estimation method).

Making a volatility forecast that is reasonably good is actually not that hard, and simple GARCH models are easy to fit and provide fairly good out-of-sample forecasting ability. So, without further ado, here are links to simple volatility models for the three major market indices: the Dow Jones Industrial Average; the S&P 500 Index; and, the NASDAQ-100 Index. This data is computed from publically available information that is believed but not guaranteed to be correct. The data is a statistically derived estimate and should be correct out-of-sample on average. It is updated daily and each estimate applies only for the day indicated in the series. For each date the annualized relative volatility (i.e. of returns) in percent, the daily point volatility, and the day's actual index point change are presented. All are from prior-close to close.