An Engineering Approach to Forecast Volatility of Financial Indices
| Main Authors: | Irwin Ma, Tony Wong, Thiagas Sankar |
|---|---|
| Format: | Article |
| Bahasa: | eng |
| Terbitan: |
, 2007
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1083435 |
Daftar Isi:
- 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.