( ISSN 2277 - 9809 (online) ISSN 2348 - 9359 (Print) ) New DOI : 10.32804/IRJMSH

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FORECASTING INDIAN VOLATILITY INDEX (INDIA VIX): A NEURAL NETWORK APPROACH

    2 Author(s):  KAUSTUVA HOTA , DR. MAHESWAR SAHU

Vol -  7, Issue- 10 ,         Page(s) : 168 - 182  (2016 ) DOI : https://doi.org/10.32804/IRJMSH

Abstract

Predicting stock market has always been a tough task for the investors as a result of which it has become a popular area of interest for the researchers. For better risk management prediction of volatility assessment is very important. This study tries predicting the Indian Volatility Index (India VIX) through neural network approach using a TDNN model. It also tries to analyze the predictive ability of the model.

1. Abhyankar, A., Copeland, L., & Wong, W. (1997). Uncovering Non-linear Structure in Real-time Stock Market Indexes: The S&P 500, the DAX, the Nikkei 225 and the FTSE-100. Journal of Business and Economic Statistics, 15(1), 1-14. 
2. Akigray, V. (1989). Conditional Heteroskedasticity in Time Series of Stock Returns: Evidence and Forecasts. Journal of Business, 62(1), 55-80. 
3. Alexander, L., & Leigh, C. (1997). Covariance Matrices Used in Value at Risk Models. Journal of Derivatives, 4(1), 50-62. 
4. Amiri, S., Von Rosen, D., & Zwanzig, S. (2009). The SVM Approach for Box-Jenkins Models. REVSTAT Statisical Journal, 7(1), 23-36. 
5. Anderson, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Makridakis, S., . . . Winkler, R. (1982). The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition. Journal of Forecasting, 1(1), 111-153. 
6. Antonakakis, N., & Scharler, J. (2012). Volatility, Information and Stock Market Crashes. Journal of Advanced Studies in Finance, 5(1), 49-67. 
7. Anwar, S., & Mikami, Y. (2011). Comparing Accuracy Performance of ANN, MLR and GARCH Models in Predicting Time Deposit Return of Islamic Bank. International Journal of Trade Economics and Finance, 2(1), 44-51. 
8. Araujo, M., & Mare, E. (2006). Examining the Volatility Skew in the South African Equity Market Using Risk-neutral Historical Distributions. Investments Analyst Journal, 64(1), 15-20. 
9. Archibald, B., & Koehler, A. (2003). Normaliztion of Seasonal Factors in Winters Methods. International Journal of Forecasting, 19(1), 143-148. 
10. Atiya, F., El-Sherif, S., El-Shoura, S., & Shaheen, S. (1999). A Comparison Between Neural-Network Forecasting Techniques - Case Study: River Flow Forecasting. IEEE Transactions on Neural Networks, 10(2), 402-409. 
11. Bardina, J., & Rajkumar, T. (2003). Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients. AeroSense 2003: SOIE's 17th Annual International Symposium on Aerospace/Defense Simulation and Controls. 2. Orlando: NASA Technical Reports Server. 
12. Bates, J., & Granger, W. (1969). The Combination of Forecasts. Operations Research Quarterly, 20(1), 541-468. 
13. Beckers, S. (1981). Standard Deviations Implied in Option Prices as Predictors of Future Stock Price Variability. Journal of Banking Finance, 5(1), 363-381.
14. Berk, J. (1995). A Critique of Size-Related Anomolies. Review of Financial Studies, 8(1), 275-286. 
15. Bollerslev. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(1), 307-327. 
16. Bollerslev, T., Chou, R., & Kroner, K. (1992). ARCH Modelling in Finance. Journal of Econometrics, 52(1), 307-327. 
17. Boudoukh, J., Richardson, M., & Whitelaw, R. (1997). Investigation of a Class of Volatility Estimators. Journal of Derivatives, 4(1), 63-71. 
18. Bowerman, B., & O'Connell, R. (1979). Time Series and Forecasting: An Applied Approach. New York: Duxbury Press. 
19. Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control (Vol. 1). San Francisco: Holden Day. 
20. Boyd, M., & Kaastra, I. (1996). Designing a Neural Network for Forecasting Financial and Economic Time Series. Neurocomputing, 10, 215-236. 
21. Brock, W., Dechert, J., Scheinkman, & LeBaron, B. (1996). A Test for Independence Based on the Correlation Dimension. Econometric Review, 15(1), 197-235. 
22. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 41(1), 1731-1764. 
23. Brownlees, C., Engle, R., & Kelly, B. (2012). A Practical Guide to Volatility Forecasting  Through Calm and Storm. The Journal of Risk, 14(2), 3-22. 
24. Cancelliere, R., & Gemello, R. (1996). Efficient Training of Time Delay Neural Networks for Sequential Patterns. Neurocomputing, 10(1), 33-42. 
25. Chen, A., Daouk, H., & Leung, M. (2000). Forecasting Stock Indices: A Comparisson of Classification and Level Estimation Models. International Journal of Forecasting, 16(1), 173-190. 
26. Chiu, C., Lee, T., & Lu, C. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems, 47(1), 115-125. 
27. Clements, M., Franses, P., & Swanson, N. (2004). Forecasting Economic Time-series with Non-linear Models. International Journal of Forecasting, 20(1), 169-183. 
28. Conrad, J., & KAul, G. (1988). Time Variation in Expected Returns. Journal of Business, 61(1), 409-425.
29. Crone, S., & Kourentzes, N. (2008). Forecasting High Freqeuency Time Series with Neural Networks - An Analysis of Modeling Challenges from Increasing Data Frequency. International Conference on Data Mining, (pp. 37-42). Las Vegas. 
30. Day, T., & Lewis, C. (1992). Stock Market Volatility and the Information Content of Stock Index Options. Journal of Economics, 52(1), 267-287. 
31. De Gooijer, J., & Hyndman, R. (2006). 25 Years of Time Series Forecasting. International Journal of Forecasting , 22(1), 443-473. 
32. Deboeck, G. (1994). Trading on the Edge: Neural, Genetic and Fuzy Systems for Chaotic Financial Markets. New York: Wiley & Sons. 
33. DeBondt, W., & Thaler, R. (1985). Does the Stock Market Overreact? Journal of Finance, 40(1), 793-805. 
34. Dickey, D., & Fuller, W. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(1), 427-431. 
35. Dixit, G., Roy, D., & Uppal, N. (2013). Predicting India Volatility Index: An Application of Artificail Neural Network. International Journal of Computer Applications, 70(4), 22-30. 
36. Dockner, E., Dorffner, G., & Schittenkopf, C. (2000). Forecasting Time-dependent Conditional Densities: A Semi-nonparametric neural network approach. Journal of Forecasting, 19(4), 355-374. 
37. Granger, C., & Poon, S. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 41(1), 478-539. 
38. Granger, C., & Poon, S. (2005). Practical Issues in Forecasting Volatility. Financial Analysts Journal, 61(1), 45-56. 
39. Hu, M., Patuwo, E., & Zhang, G. (1998). Forecasting with Artificial Neural Networks: The State of the Art. 14(1), 35-62. 
40. Marcellino, M. (2007). A Comparisson of Time Series Models for Forecasting GDP Growth and Inflation. Unpublished Thesis, European University Institute, Department of Economics, Milan. 
41. Marucci, J. (2005). Forecasting Stock Market Volatility with Regime-Switching GARCH Models. Unpublished Thesis, University of California, Department of Economics, California. 
42. Merton, R. (1980). On Estimating the Expected Return on the Market: An Exploratory Investigation. Journal of Financial Economics, 8(1), 323-361. 
43. Miller, D., & Williams, D. (1999). Level-Adjusted Exponential Smoothing Modelling for Planned Discontinuities. International Journal of Forecasting, 15(1), 273-289. 
44. Muth, J. (1960). Optimal Properties of Exponentially Weighted Forecasts. Journal of the American Statistical Association, 55(1), 299-306. 
45. Nelson, C., & Plosser, C. (1982). Trends and Random Walks in Macroeconomic Time Series. Journal of Monetory Economics, 10(1), 139-162. 
46. Nelson, D. (1992). Filtering and Forecasting with Misspecified ARCH Models I: Getting the Right Variance with the Wrong Model. Journal of Econometrics, 52(1), 61-90. 
47. Pissarenko. (2002). Neural Networks for Financial Time Series Prediction: Overview Over Recent Research. Unpublished Thesis, Department of Computer Science. 
48. Poon, S. (2008, August 11). Historical Volatility Models. Retrieved August 2, 2013, from https://phps.portals.mbs.ac.uk/Portals/49/docs/spoon/HisVol.pdf.pdf 
49. Qi, Z., & Zhang, G. (2005). Neural Network Forecasting for Seasonal and Trend Time Series. European Journal of Operational Research, 160, 501-514. 
50. Rapach, D., & Zhou, G. (2013). Forecasting Stock Returns. In G. Elliot, & A. Timmermann, Handbook of Economic Forecasting (Vol. 2, pp. 328-383). 
51. Roberts, S. (1982). A General Class of Holt-Winters Type Forecasting Models. Management Science, 28(1), 808-820. 
52. Roll, R. (1984). A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficeint Market. Journal of Finance, 39(1), 1127-1140. 
53. Rozeff, P. (1984). Dividend Yields are Equtiy Premiums. Journal of Portfolio Management, 1(1), 68-75. 
54. Sabbatini, M., & Linton, O. (1998). A GARCH Model of the Implied Volatility of the Swiss Market Index from Option Prices. International Journal of Forecasting, 14(1), 199-213.
55. Saleemah, A. (2012). A Multilayer Perceptron for Predicting Shear Strength of Reinforced Concrete Beams. Journal of Civil Engineering and Construction Technology, 3(2), 64-79. 
56. Samouilhan, N., & Shannon, G. (2008). Forecasting Volatility on the JSE. Investment Analysts Journal, 67(1), 18-28. 
57. Satchell, S., & Timmermann, A. (1995). On the Optimality of Adaptive Expectations: Muth Revisited. International Journal of Forecasting, 11(3), 407-416. 
58. Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibtrium Under Conditions of Risk. Journal of Finance, 19(1), 425-442. 
59. Shumway, R., & Stoffer, D. (2011). Time Series Analysis and Its Applications With R Examples. New York: Springer Science+Business Media. 
60. Stuart, H. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. 
61. Sullivan, R., Timmerman, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance and the Bootstrap. Journal of Finance, 54(1), 1647-1691. 
62. Taylor, S. (1987). Forecasting the Volatility of Currency Exchange Rates. Internatioanl Journal of Forecasting, 3(1), 159-170. 
63. Thiel, H. (1966). Applied Economic Forecasting. Chicago: Rand McNally. 
64. Tse, Y. (1991). Stock Reurn Volatility in the Tokyo Stock Market. Japan and the World Economy, 3(1), 285-298. 
65. Tse, Y., & Tung, S. (1992). Forecasting Volatility in the Singapore Stock Market. Asia Pacific Journal of Management, 9(1), 1-13. 
66. Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioural Sciences. Unpublished Thesis, Harvard University. 
67. White, H. (2000). A Reality Check for Data Snooping. Econometrica, 68(5), 1097-1126. 
68. Wilamowski, M., & Yu, H. (2011). Levenberg-Marquardt Training. The Industrial Electronics Handbook, 5, pp. 12-27. 
69. Yule, G. (1927). On the Method of Investigating Periodicities in Disturbed Series with Special Reference to Wolfer's Sunspot Nmbers. Philosophical Transactions of the Royal Society of London, 226, 267-298.
70. Zhang, G. (2001). An Investigation of Neural Network for Linear Time-series Forecasting. European Computers and Operational Research, 28(12), 1183-1202.
71. Trippi, R. and E. Turban (1993), eds., Neural Networks in Finance and Investing, Probus Publishing Company.
72. Trippi, R. R. and D. Desieno (1992), "Trading equity index futures with a neural network", Journal of Portfolio Management, 19, 27-33.

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