An Engineering Approach to Forecast Volatility of Financial Indices
By systematically applying different engineering
methods, difficult financial problems become approachable. Using a
combination of theory and techniques such as wavelet transform,
time series data mining, Markov chain based discrete stochastic
optimization, and evolutionary algorithms, this work formulated a
strategy to characterize and forecast non-linear time series. It
attempted to extract typical features from the volatility data sets of
S&P100 and S&P500 indices that include abrupt drops, jumps and
other non-linearity. As a result, accuracy of forecasting has reached
an average of over 75% surpassing any other publicly available
results on the forecast of any financial index.
[1] Andersen, T., Bollerslev, T., Diebol, F. and Labys, P., "Modeling and
Forecasting realized volatility ", First Draft: January 1999, This Version:
January 2001
[2] Ma, I., Wong, T., Sankar, T., & Siu, R., "Volatility Forecasts of the
S&P100 by Evolutionary Programming in a Modified Time Series Data
Mining Framework", WAC 2004 conference, Seville, Jun. 2004a
[3] Andersen, T. G., T. Bollerslev, F. X. Diebold. and P. Labys. 2001, "The
distribution of realized exchange rate volatility," Journal of the
American Statistical Association, no. 96, pp. 42-55
[4] Kinlay, H. R., J. Neftci and P. S. Wilmott, 2001, "Investment analytics
volatility report," Investment Analytics, N.Y.
[5] Christoffersen, P. and F. Diebold, 2000, "How relevant is volatility
forecasting for financial risk management?," Review of Economics and
Statistics, vol. 82, pp. 12-22
[6] Ma, I., Wong, T., Sankar, T., & Siu, R., "Forecasting the Volatility of a
Financial Index by Wavelet Transform and Evolutionary Algorithm",
IEEE SMC 2004 Conference, Hague, Oct. 2004
[7] Pictet, O. V., Masutti, O. and Zumbach, G., "Volatility forecasting using
genetic programming", Dynamic Asset Management, Geneva,
Switzerland and Olsen & Associates, Zurich, Switzerland, 2000
[8] Zumbach, G., Pictet, O. V. and Masutti O., "Genetic Programming with
Syntactic Restrictions applied to Financial Volatility Forecasting", Olsen
& Associates Research Institute for Applied Economics Seefeldstrasse
233, 8008 Zurich, Switzerland, Dynamic Asset Management Chemin des
Tulipiers 9, 1208, Geneva, Switzerland, 2001
[9] Chen, Shu-Heng and Yeh, Chia-Hsuan, "Using Genetic Programming to
Model Volatility in Financial Time Series: The Cases of Nikkei 225 and
S&P 500", AI-Econ Research Group, Dept. of Economics, National
Chengchi University, Taipei, Taiwan 1623, The 4th JAFEE International
Conference on Investments and Derivatives (JIC-97), Royal Park Hotel,
Tokyo, Japan, Jul. 31-Aug. 1, 1997
[10] Neely, C. J. and P. Weller, "Predicting Exchange Rate Volatility:
Genetic Programming vs. GARCH and RiskMetrics", Research
Department, Federal Reserve Bank of St. Louis and Department of
Finance, Henry B. Tippie College of Business, Sept. 2001
[11] Kaboudan, M, "Extended Daily Exchange Rates Forecasts Using
Wavelet Temporal Resolutions", New Mathematics and Natural
Computation, Vol. 1, No. 1, by World Scientific Publishing Co., 2005,
p. 79- 107
[12] Lee, W. C., "Forecasting High-Frequency Financial Data Volatility Via
Nonparametric Algorithms -Evidence from Taiwan Financial Market",
4th International Conference on Computational Intelligence in
Economics and Finance, Salt Lake City, Utah, Jul. 2005, p. 1031 - 1034
[13] Lawrenz, C. and Westerhoff F., "Explaining Exchange Rate Volatility
with a Genetic Algorithm", 6th International conference of the Society
for computational Economics on Computing in Economics and Finance,
Barcelona, paper 325, Jul. 2001
[14] Fong, L. Y. and Szeto, K. Y., "Rules Extraction in Short Memory Time
Series Using Genetic Algorithms", The European Physical Journal B,
20, pp. 569-572, 2001
[15] Maheu, J. and McCurdy, T., "Nonlinear Features of Realized FX
Volatility", Department of Economics, University of Alberta and
Rotman School of Management, University of Toronto
[16] Gaunersdorfer C. and Hommes Cars, "A Nonlinear Structural Model for
Volatility Clustering", Center for Nonlinear Dynamics in Economics and
Finance (CeNDEF) Faculteit Economie, Universiteit van Amsterdam,
CeNDEF Working Papers, number 00-02, Sept. 2000
[17] Dunis, C. and X. Huang , "Forecasting and Trading Currency Volatility:
An Application of Recurrent Neural Regression and Model
Combination", The Journal of Forecasting, 21, 317-354., 2002
[18] Wang, N. J., K. Wang and J. Jeng, "An Empirical Approach toward
Realistic Modeling of Capital Market Volatility", 4th International
Conference on Computational Intelligence in Economics and Finance,
Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[19] Hovsepian, K, Anselmo P. C., and Mazumdar, S., "Detection and
Prediction of Relative Clustered Volatility in Financial Markets", 4th
International Conference on Computational Intelligence in Economics
and Finance, Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[20] Tino, P., Nikolaev, N. and Yao, X., "Volatility Forecasting with Sparse
Bayesian Kernel Models", 4th International Conference on
Computational Intelligence in Economics and Finance, Salt Lake City,
Utah, Jul. 2005, p. 1052 - 1058
[21] Gavrishchaka1, G. G, "Boosting Frameworks in Financial Applications:
From Volatility Forecasting to Portfolio Strategy Optimization", 4th
International Conference on Computational Intelligence in Economics
and Finance, Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[22] Povinelli, R. J., 1999, "Time Series Data Mining: Identifying Temporal
Patterns for Characterization and Prediction of Time Series Events",
Marquette University, Ph. D. dissertation
[23] Povinelli, R. J., "Identifying Temporal Patterns for Characterization and
Prediction of Financial Time Series Events," Temporal, Spatial and
Spatio-Temporal Data Mining: First International Workshop; revised
papers / TSDM2000, 46-61, 2000
[24] Andradottir, S., "Accelerating the Convergence of Random Search
Methods for Discrete Stochastic Optimization", ACM Transactions on
Modeling and Computer Simulation, Vol. 9, No. 4, Oct. 1999
[25] Duan, J. C., Gautier, G. and Simonato, J. G, "A Markov Chain Method
for Pricing Contingent Claims", Stochastic Modeling and Optimization -
with applications in queues, finance, and supply chains, Springer-
Verlag, New York, Inc., 2003
[1] Andersen, T., Bollerslev, T., Diebol, F. and Labys, P., "Modeling and
Forecasting realized volatility ", First Draft: January 1999, This Version:
January 2001
[2] Ma, I., Wong, T., Sankar, T., & Siu, R., "Volatility Forecasts of the
S&P100 by Evolutionary Programming in a Modified Time Series Data
Mining Framework", WAC 2004 conference, Seville, Jun. 2004a
[3] Andersen, T. G., T. Bollerslev, F. X. Diebold. and P. Labys. 2001, "The
distribution of realized exchange rate volatility," Journal of the
American Statistical Association, no. 96, pp. 42-55
[4] Kinlay, H. R., J. Neftci and P. S. Wilmott, 2001, "Investment analytics
volatility report," Investment Analytics, N.Y.
[5] Christoffersen, P. and F. Diebold, 2000, "How relevant is volatility
forecasting for financial risk management?," Review of Economics and
Statistics, vol. 82, pp. 12-22
[6] Ma, I., Wong, T., Sankar, T., & Siu, R., "Forecasting the Volatility of a
Financial Index by Wavelet Transform and Evolutionary Algorithm",
IEEE SMC 2004 Conference, Hague, Oct. 2004
[7] Pictet, O. V., Masutti, O. and Zumbach, G., "Volatility forecasting using
genetic programming", Dynamic Asset Management, Geneva,
Switzerland and Olsen & Associates, Zurich, Switzerland, 2000
[8] Zumbach, G., Pictet, O. V. and Masutti O., "Genetic Programming with
Syntactic Restrictions applied to Financial Volatility Forecasting", Olsen
& Associates Research Institute for Applied Economics Seefeldstrasse
233, 8008 Zurich, Switzerland, Dynamic Asset Management Chemin des
Tulipiers 9, 1208, Geneva, Switzerland, 2001
[9] Chen, Shu-Heng and Yeh, Chia-Hsuan, "Using Genetic Programming to
Model Volatility in Financial Time Series: The Cases of Nikkei 225 and
S&P 500", AI-Econ Research Group, Dept. of Economics, National
Chengchi University, Taipei, Taiwan 1623, The 4th JAFEE International
Conference on Investments and Derivatives (JIC-97), Royal Park Hotel,
Tokyo, Japan, Jul. 31-Aug. 1, 1997
[10] Neely, C. J. and P. Weller, "Predicting Exchange Rate Volatility:
Genetic Programming vs. GARCH and RiskMetrics", Research
Department, Federal Reserve Bank of St. Louis and Department of
Finance, Henry B. Tippie College of Business, Sept. 2001
[11] Kaboudan, M, "Extended Daily Exchange Rates Forecasts Using
Wavelet Temporal Resolutions", New Mathematics and Natural
Computation, Vol. 1, No. 1, by World Scientific Publishing Co., 2005,
p. 79- 107
[12] Lee, W. C., "Forecasting High-Frequency Financial Data Volatility Via
Nonparametric Algorithms -Evidence from Taiwan Financial Market",
4th International Conference on Computational Intelligence in
Economics and Finance, Salt Lake City, Utah, Jul. 2005, p. 1031 - 1034
[13] Lawrenz, C. and Westerhoff F., "Explaining Exchange Rate Volatility
with a Genetic Algorithm", 6th International conference of the Society
for computational Economics on Computing in Economics and Finance,
Barcelona, paper 325, Jul. 2001
[14] Fong, L. Y. and Szeto, K. Y., "Rules Extraction in Short Memory Time
Series Using Genetic Algorithms", The European Physical Journal B,
20, pp. 569-572, 2001
[15] Maheu, J. and McCurdy, T., "Nonlinear Features of Realized FX
Volatility", Department of Economics, University of Alberta and
Rotman School of Management, University of Toronto
[16] Gaunersdorfer C. and Hommes Cars, "A Nonlinear Structural Model for
Volatility Clustering", Center for Nonlinear Dynamics in Economics and
Finance (CeNDEF) Faculteit Economie, Universiteit van Amsterdam,
CeNDEF Working Papers, number 00-02, Sept. 2000
[17] Dunis, C. and X. Huang , "Forecasting and Trading Currency Volatility:
An Application of Recurrent Neural Regression and Model
Combination", The Journal of Forecasting, 21, 317-354., 2002
[18] Wang, N. J., K. Wang and J. Jeng, "An Empirical Approach toward
Realistic Modeling of Capital Market Volatility", 4th International
Conference on Computational Intelligence in Economics and Finance,
Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[19] Hovsepian, K, Anselmo P. C., and Mazumdar, S., "Detection and
Prediction of Relative Clustered Volatility in Financial Markets", 4th
International Conference on Computational Intelligence in Economics
and Finance, Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[20] Tino, P., Nikolaev, N. and Yao, X., "Volatility Forecasting with Sparse
Bayesian Kernel Models", 4th International Conference on
Computational Intelligence in Economics and Finance, Salt Lake City,
Utah, Jul. 2005, p. 1052 - 1058
[21] Gavrishchaka1, G. G, "Boosting Frameworks in Financial Applications:
From Volatility Forecasting to Portfolio Strategy Optimization", 4th
International Conference on Computational Intelligence in Economics
and Finance, Salt Lake City, Utah, Jul. 2005, p. 1052 - 1058
[22] Povinelli, R. J., 1999, "Time Series Data Mining: Identifying Temporal
Patterns for Characterization and Prediction of Time Series Events",
Marquette University, Ph. D. dissertation
[23] Povinelli, R. J., "Identifying Temporal Patterns for Characterization and
Prediction of Financial Time Series Events," Temporal, Spatial and
Spatio-Temporal Data Mining: First International Workshop; revised
papers / TSDM2000, 46-61, 2000
[24] Andradottir, S., "Accelerating the Convergence of Random Search
Methods for Discrete Stochastic Optimization", ACM Transactions on
Modeling and Computer Simulation, Vol. 9, No. 4, Oct. 1999
[25] Duan, J. C., Gautier, G. and Simonato, J. G, "A Markov Chain Method
for Pricing Contingent Claims", Stochastic Modeling and Optimization -
with applications in queues, finance, and supply chains, Springer-
Verlag, New York, Inc., 2003
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63682", author = "Irwin Ma and Tony Wong and Thiagas Sankar", title = "An Engineering Approach to Forecast Volatility of Financial Indices", abstract = "By systematically applying different engineering
methods, difficult financial problems become approachable. Using a
combination of theory and techniques such as wavelet transform,
time series data mining, Markov chain based discrete stochastic
optimization, and evolutionary algorithms, this work formulated a
strategy to characterize and forecast non-linear time series. It
attempted to extract typical features from the volatility data sets of
S&P100 and S&P500 indices that include abrupt drops, jumps and
other non-linearity. As a result, accuracy of forecasting has reached
an average of over 75% surpassing any other publicly available
results on the forecast of any financial index.", keywords = "Discrete stochastic optimization, genetic algorithms, genetic programming, volatility forecast", volume = "1", number = "10", pages = "573-13", }
