Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper concentrates on the modelling and trading of three daily market implied volatility indices issued on the Chicago Board Options Exchange (CBOE) using evolving combinations of prominent autoregressive and emerging heuristics models, with the aims of introducing an algorithm that provides a better approximation of the most popular U.S. volatility indices than those that have already been presented in the literature and determining whether there is the ability to produce profitable trading strategies. A heterogeneous autoregressive process (HAR) is combined with a genetic algorithm–support vector regression (GASVR) model in two hybrid algorithms. The algorithms’ statistical performances are benchmarked against the best forecasters on the VIX, VXN and VXD volatility indices. The trading performances of the forecasts are evaluated through a trading simulation based on VIX and VXN futures contracts, as well as on the VXZ exchange traded note based on the S&P 500 VIX mid-term futures index. Our findings indicate the existence of strong nonlinearities in all indices examined, while the GASVR algorithm improves the statistical significance of the HAR processes. The trading performances of the hybrid models reveal the possibility of economically significant profits.