# How would you forecast volatility without using any programming languages or machine learning or anything of that sort?

I am trying to forecast volatility. I am on the tactical asset allocation team. No one on our team knows machine learning or any programming languages. We are fundamental equity research analysts trying to find a way to forecast volatility. We were thinking of maybe using the VIX futures?

For asset allocation purposes I would use implied volatility on atmf options on the underlying with a maturity close to the term in which you are interested. There will be some premium in there so you can run a regression and find out how much premium on average is in there historically, or you can just divide by 1.1, which is a good approximation for the premium.

Example: Say 1mo S&P atmf options trade with implied volatility of 50, then your estimate for 1mo vol is 50/1.1 = 45.5

• This is super helpful! Just curious as to why 1.1 is a good approximation for the premium? Apr 15, 2020 at 3:14
• @Winnie 1.1 just happens to be what the premium is if you look at some long run average of (3mo or 1yr implied / subsequent 3mo or 1yr realized). 1.07 is actually the figure i recall. so if 3mo at the money implied vol is 15, then 15/1.07 = your estimate for 3mo realized vol. Apr 15, 2020 at 17:23
• Just pull your own data. You can do this in excel with yahoo finance or something. Apr 27, 2020 at 19:46

Basically, you have to choose whether to use a forward-looking or a backward-looking method of forecasting volatility. Let's start with the VIX. The VIX is an implied volatility index. Option pricing models require the volatility of the underlying asset as an input. Volatility is not an observed quantity, so the people who are pricing the options have to estimate it. This means that you can plug the market price of the option back into the pricing formula, and solve it backwards for the volatility, which will then roughly correspond to the market's estimate of what the volatility will be during the maturity period of the option. The VIX is an index that tracks this implied volatility, the underlying being the S&P 500 index. It used to be calculated on the S&P 100 index using index options, but nowadays the CBOE has switched the methodology to using the broader S&P 500 and a "variance swap"-based calculation. The interpretation is however basically the same, it measures how large the volatility is expected to be over the next 12 months. This is a forward-looking volatility measure: It incorporates information of what the market believes that the volatility will be in the future. See this whitepaper for more details.

VIX futures are futures on implied volatility. This means that their payoff is based on what the market, at some time in the future, will believe that the volatility will be during some maturity period. I am not sure why you would use futures on the VIX rather than just using the VIX itself.

The alternative is a backward-looking measure, i.e. forecasting volatility tomorrow based on what it has been during some period in the (recent) past. Then, a good place to start would be GARCH models (Generalized Autoregressive Conditional Heteroskedasticity). This is a (very) broad class of models, but I'd say that for equity, you might want to look into the GJR-GARCH model of Glosten, Jagannathan and Runkle (1993) or the E-GARCH model of Nelson (1991). The volatility of equity tends to be asymmetric, i.e. negative shocks might affect volatility more harshly as compared to positive shocks. The GJR- and EGARCH models take this into account.

Becker et. al (2007) compare implied volatility-based models to the performance of other types of volatility models. Many of these are very involved. I want to emphasize that forecasting volatility is a difficult endeavour, and from a risk-management perspective, there are arguments in favour of the view that one should not even attempt it. It can give you a false sense of security. Any volatility forecast should not be interpreted as certain, but rather as an indication that is prone to being terribly wrong.

• A very warm welcome to Quant.SE! Very good and comprehensive answer (+1) Apr 8, 2020 at 16:22