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Open AccessArticle

Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non-Linear Models

by David E Allen 1,2,3,* and Vince Hooper 4
School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia
Department of Finance, Asia University, Taichung 41354, Taiwan
School of Business and Law, Edith Cowan University, Edith Cowan University, Joondalup 6027, Australia
School of Economics and Management, Xiamen University, 43900 Sepang, Selangor Darul Ehsan, Malaysia
Author to whom correspondence should be addressed.
Sustainability 2018, 10(8), 2695;
Received: 7 June 2018 / Revised: 18 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Risk Measures with Applications in Finance and Economics)
This paper features an analysis of causal relations between the daily VIX, S&P500 and the daily realised volatility (RV) of the S&P500 sampled at 5 min intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the future daily value of the VIX. Causal relations are analysed using the recently developed concept of general correlation Zheng et al. and Vinod. The neural network analysis is performed using the Group Method of Data Handling (GMDH) approach. The results suggest that causality runs from lagged daily RV and lagged continuously compounded daily return on the S&P500 index to the VIX. Sample tests suggest that an ANN model can successfully predict the daily VIX using lagged daily RV and lagged daily S&P500 Index continuously compounded returns as inputs. View Full-Text
Keywords: GMC; VIX; RV5MIN; causal path; ANN GMC; VIX; RV5MIN; causal path; ANN
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Allen, D.E.; Hooper, V. Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non-Linear Models. Sustainability 2018, 10, 2695.

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