All the major market indices had massive spikes in volatility at the end of last year. I use a common paradigm to model all of them. A simple GARCH model to forecast daily price volatility. These models were all developed on prior data and are running out of sample.

We looked at the volatility of the DJIA relative to its history in a recent post. Presented here is the same chart built for the S&P 500 Index, which is popular with institutional fund managers but has an inbuilt large-cap bias and does not represent a mimumum variance portfolio. Although the member selection method is less arbitary than that for the Dow, it is still not 100% mechanical.

We see the S&P volatility has also reduced and is at the upper end of the "normal" range. Like the Dow analysis, the volatility model is presented in terms of daily point move (for clarity of exposition) and is fitted with driving innovations that are intrinsically leptokurtotic — i.e. we model the fat tails as an intrinsic property of the driving process and not solely attributable to the composite nature of GARCH type process. The
generalized error distribution is used to model the i.i.d. innovations.