Forecasting VIX using Bayesian Deep Learning

Investors and regulators are concerned about financial market volatility and crashes. For this reason, the Volatility index (VIX) was introduced in 1993 by the Chicago Board Options Exchange (CBOE) with the aim of assessing the expected financial market volatility in the short-run, i.e. for the next 30 days, since it is calculated as an implied volatility from the options on the S&P 500 index on this time-to-maturity [1]. The VIX has been proven to be a good predictor of expected stock index shifts, and therefore as an early warning for investor sentiment and financial market turbulences (see e.g., [1], and more recently, [2]). Due to its importance for asset managers and regulators, it would be useful to foresee the values of the index; however, the VIX is very difficult to forecast [3]. There exist several proposals to predict time series found in the literature classified as conventional and modern methods (see e.g., [4] and the references therein).