This paper concentrates on modelling and trading three daily market implied volatility indices issued on the Chicago Board Options Exchange (CBOE) with evolving combinations of prominent autoregressive and emerging heuristics models. The motivation is to introduce an algorithm that provides a better approximation of the most popular U.S. volatility indices than the algorithms already presented in the literature and determine whether there is a capability of producing 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 performance is benchmarked against the best forecasters on the VIX, VXN and VXD volatility indices respectively. The trading performance of the forecasts is 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 that strong nonlinearities exist in all indices examined, while the GASVR algorithm improves the statistical significance of HAR processes. The trading performance of the hybrid models reveals the possibility of economically significant profits.
This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan et al. (1999), divided into five families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages), is applied to the daily prices of West Texas Intermediate (WTI) light, sweet crude oil futures as well as the United States Oil (USO) fund, from 2006 onwards. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano and Wolf (2007) and Bajgrowicz and Scaillet (2012) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature in rules performance, contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of interim market inefficiencies.
This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan et al. (1999), divided into five families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages), is applied to the daily prices of West Texas Intermediate (WTI) light, sweet crude oil futures as well as the United States Oil (USO) fund, from 2006 onwards. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano and Wolf (2007) and Bajgrowicz and Scaillet (2012) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature in rules performance, contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of interim market inefficiencies.
In this study a Krill Herd-Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity Exchange Traded Funds (ETFs) on a daily basis over the period 2012-2014. The inputs of the KH-vSVR models are selected through the Model Confidence Set (MCS) from a large pool of linear predictors. The KH-vSVR's statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on Heterogeneous Autoregressive (HAR) volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.
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