Modeling Evolutionary Neural Network to Evaluate Financial Time-Series Confidence Interval of Dynamic VaR

• Main Authors: Hsio-Yi Lin, 林秀怡
• Other Authors: An-Pin Chen
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/68299277155906934765

博士 === 國立交通大學 === 資訊管理研究所 === 96 === The appraisement of asset price/return and volatility has always been the concerned topic in field of financial time series by the financial economists. After the introduction of the Autoregressive Conditional Heteroscedastic Model (ARCH) by Engle (1982) and the extension version of Generalized Autoregressive Conditional Heteroscedastic Model (GARCH) by Bollserlev (1986), these two models are wildly adopted in analyzing financial time-series data, and further prove the feature of “time-varying” of return on assets. In addition, several literatures also pointed out the physical phenomenon, “momentum”, exists in financial market. Nevertheless, the GARCH model only considers conditional variance and sum of squared residual as the variables in explaining volatility, but fails to cover the influence of factors of behavioral finance on volatility. In recent years, the applications of artificial intelligence in financial market are appropriate for solving complicated non-linear problems, since they do not have to deal with many assumptions and limitations within conventional financial mathematics and statistical model. This research introduces an interval-oriented neural network (Neuro-Fuzzy BPN, called NF BPN) that is appropriate for assessing financial time-series data. Despite refining the flaws of point estimation in traditional neural network, it also reserved the non-linear predictive capability of neural network, to enhance the GARCH model in the insufficiency of independent variables. Also, it includes the price-quantity technical indexes and momentum of variances from financial time series as explanatory variables, to improve the details of evaluation in asset price/volatility of return rate. Besides, the trend of international financial liberation, loosening of regulation and open-up financial environment stimulate the emphasis of risk management. Value at Risk (VaR) model is able to quantify risk and wildly adopted by investors and financial institutes. Therefore, this research constructed the dynamic VaR model according to NF BPN, which may effectively capture the clustering phenomenon on asset prices/volatility of return rate, and further provide the investors and financial institutes as reference for risk management.This research takes the Taiwan Top 50 Tracker Fund (TTT 50) as empirical data to analyze, after comparing the empirical results and conventional GARCH model by evaluating their MSE, MAE and MAPE, the interval-base neural network model has the best prediction. In viewing the three features of accuracy, conservativeness and efficiency, the NF BPN has the better accuracy. The better conservativeness and efficiency of the GARCH model is meaningless under its weak accuracy. In general, this research suggests an innovative interval-oriented BPN model to evaluate the dynamic VaR of asset price/return and volatility, which may effectively apply the financial time-series data to assessing the possible loss under risk management. The physical momentum for price-quantity technical indicator and variance is also applied to enhance the explanatory capability of the model, to support the decision making process of the investors and financial institutes. Many other explanatory indexes are expected to include in the model to construct an excellent mechanism for risk management in the future, and the model may further be applied in other fields.