Wo-Chiang Lee

Hedging Derivative Securities with Genetic Programming

One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulas. Earlier studies take the Black–Scholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black–Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20% of the 97 test paths GP has a lower tracking error than the Black–Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP. Copyright  1999 John Wiley & Sons, Ltd.

Redefinition of the KMV model's optimal default point based on genetic algorithms - Evidence from Taiwan

In this paper, we propose a new method based on genetic algorithms to solve the optimal default point of the KMV model. In our empirical study, we compare the GA-KMV model with the QR-KMV and KMV models. The results indicate that the percentage of correctness of the GA-KMV model is higher than those for the other two models. This is to say, the GA-KMV model has a better goodness of fit. We also obtain the optimal default point for a Taiwan listed company. This can help us to predict the default point and improve the banks risk management performance.

Threshold effects in the relationships between USD and gold futures by panel smooth transition approach

Using a Panel Smooth Transition Regression (PSTR) model, this study sets crude oil as threshold variable, and Volatility Index (VIX) and Morgan Stanley Capital International (MSCI) for Emerging Market Index (MSCI-E) as control variables to investigate the nonlinear dynamic relationship between USD/yen and gold futures in the Commodity Exchange, Inc. (COMEX). Empirical results show that the transition function is a logistic type. In region 1, the price of crude oil is low. The sign of VIX is positive. USD/yen exerts negative impact on gold market due to the way that gold market functions as a factor of hedge against portfolio and geopolitical risk. In region 2, the price of crude oil is higher (the demand for crude oil may be stronger). The economy is prosperous; VIX turns low; USD/yen increases. Investors have more money from other financial markets to buy gold, thus, causing gold futures price to rise. Besides, gold is both a hedge and a safe haven for developing countries but not for emerging countries; therefore, the relationships between gold and MSCI-E are positive in both regions.

Improving financial distress prediction via genetic programming decision tree-evidence from Taiwan

Abstract In this paper, we apply the classifiers like CART, C5.0, GP decision tree and compare with Logic model and ANN model for Taiwan listed electronic companies’s bankruptcy prediction. Our empirical results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k -fold cross validation test in out- sample. That is to say, GP decision tree model has the highest accuracy and lowest expected misclassification costs. It can provide an efficient alternative to discriminate financial distress problems in Taiwan.