Predicting India Volatility Index: An Application of Artificial Neural Network

has always been an area of interest for the researchers in various realms of finance especially in the stock market e.g. stock index, return on a stock, etc. Stock market volatility is one such area. Since the inception of implied volatility index (VIX) by the Chicago Board of Options Exchange (CBOE) in 1993, VIX index has generated a lot of interest. This study examines the predicting ability of several technical indicators related to VIX index to forecast the next trading day's volatility. There is a wide set of methods available for forecasting in finance. In this study, Artificial neural network (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day's volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based indicators. The results of the study reveal that ANN models can be real handy in forecasting the downwards movement in VIX. The knowledge about a more probable downwards movement in volatility might be significant value add for the investors and help them in making decisions related to trading.

[1]  Jón Dańıelsson.  Stochastic volatility in asset prices estimation with simulated maximum likelihood , 1994 .

[2]  Steven H. Kim,et al.  Graded Forecasting using an Array of Bipolar Predictions: Application of Probabilistic Neural Networks to a Stock Market Index , 1998 .

[3]  Salvador Torra,et al.  STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index , 2005 .

[4]  Emin Avcı,et al.  Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models , 2007 .

[5]  R. Engle.  Statistical Models for Financial Volatility , 1993 .

[6]  V.P. Plagianakos,et al.  Spiking neural network training using evolutionary algorithms , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[7]  Ah Chung Tsoi,et al.  Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference , 2001, Machine Learning.

[8]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[9]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[11]  S. Satchell,et al.  Forecasting Volatility in Financial Markets : A Review , 2004 .

[12]  L. Harris,et al.  A maximum likelihood approach for non-Gaussian stochastic volatility models , 1998 .

[13]  Laurence A. Connors,et al.  Volatility Index (VIX) , 2012 .

[14]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[15]  K. Singleton.  Estimation of affine asset pricing models using the empirical characteristic function , 2001 .

[16]  Ali Ihsan Diler.  Forecasting the Direction of the ISE National-100 Index By Neural Networks Backpropagation Algorithm , 2003 .

[17]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[18]  Stephen Taylor,et al.  Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns , 2000 .

[19]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[20]  Kyoung-jae Kim.  Artificial neural networks with evolutionary instance selection for financial forecasting , 2006, Expert Syst. Appl..

[21]  D. Duffie,et al.  Simulated Moments Estimation of Markov Models of Asset Prices , 1990 .

[22]  Chung Kim Kwong,et al.  Financial Forecasting Using Neural Network or Machine Learning Techniques , 2001 .

[23]  Stavros Degiannakis,et al.  ARCH Models for Financial Applications , 2010 .

[24]  The VIX Volatility Index , 2011 .

[25]  Ramazan Gençay,et al.  Optimization of technical trading strategies and the profitability in security markets , 1998 .

[26]  A. Pestronk.  Histology of the Nervous System of Man and Vertebrates , 1997, Neurology.

[27]  P. McNelis,et al.  The effect of the Nikkei and the S&P on the All‐Ordinaries: A comparison of three models , 1998 .

[28]  Emi Nakamura,et al.  Inflation forecasting using a neural network , 2005 .

[29]  Oliver Vornberger,et al.  Sales forecasting using neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[30]  Bent E. Sørensen,et al.  GMM and QML asymptotic standard deviations in stochastic volatility models: Comments on Ruiz (1994) , 1997 .

[32]  Fred Collopy,et al.  How effective are neural networks at forecasting and prediction? A review and evaluation , 1998 .

[33]  Alan G. White,et al.  The Pricing of Options on Assets with Stochastic Volatilities , 1987 .

[34]  G. David Garson,et al.  Neural Networks: An Introductory Guide for Social Scientists , 1999 .