TY - JOUR
T1 - Modelling and trading the U.S. implied volatility indices
T2 - Evidence from the VIX, VXN and VXD indices
AU - Psaradellis, Ioannis
AU - Sermpinis, Georgios
N1 - The authors would like to acknowledge gracious support of this work from the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk and Uncertainty in Complex Systems and Environment (EP/L015927/1). We would like also to thank Fundboiler for their support.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - 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.
AB - 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.
KW - implied volatility indices
KW - heterogeneous autoregression
KW - heuristics
KW - volatility derivatives
KW - exchange traded notes
U2 - 10.1016/j.ijforecast.2016.05.004
DO - 10.1016/j.ijforecast.2016.05.004
M3 - Article
SN - 0169-2070
VL - 32
SP - 1268
EP - 1283
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 4
ER -