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An Application of Panel Threshold Effect Model Valuing American Options under ARMA Processes Chou-Wen Wang* Department of Risk Management and Insurance, National Kaohsiung First University of Science and Technology. [email protected] Chin-Wen Wu Department of Public Finance and Taxation, Meiho Institute of Technology, Pingtong, Taiwan [email protected] Abstract Motivated by the empirical findings that asset returns are autocorrelated, this paper provides the pricing algorithm for American options on the stocks, the returns of which depend on an autoregressive moving average (ARMA) process, by incorporating with the least squares Monte Carlo approach of Longstaff and Schwartz (2001) and the local risk-neutralization principle of Duan (1995). Based on the results of numerical analyses, the ARMA effect has significant impacts on values of American options. Specifically, the AR effect is more significant than the MA effect. Keywords: ARMA Process, American Option Pricing, Least Squares Monte Carlo Simulation A Study on Performance Measurement of High-tech Industry: An Application of Panel Threshold Effect Model Abstract This research discusses that, under the fluctuation of the debt ratio, how the financial ratios affect the operational performance, such as rate of return on assets (ROA). The study applies panel data threshold effect model (Hansen, 1999, 2000). The sample is gathered from the annual data of the listed companies in the high-tech industry in America from 1999 to 2004. In order to make different threshold models objectively, this study uses debt ratio as the threshold variable. The study adopts the debt ratio to determine the threshold models for ROA of high-tech industry. In the end, the empirical result gets one threshold and also shows that compared with the traditional linear regression, the threshold model, which is determined by the debt ratio, can get higher coefficient of determination. Keywords: panel threshold effect model; debt ratio; operational performance Design Variables Optimization of Mechanical Problems Kuo-Ming Lee1,2, Jinn-Tsong Tsai3, Wen-Hsien Ho4, Tung-Kuan Liu1, Jyh-Horng Chou1 1.Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan. (email: [email protected], [email protected], [email protected]) 2.Metal Industries R&D Centre, 1001 Kaonan Highway, Kaohsiung 811, Taiwan. 3.Department of Computer Science, National Pingtung University of Education, 4-18 Ming Shen Road, Pingtung 900, Taiwan. (email: [email protected]) 4.Department of Medical Information Management, Kaohsiung Medical University, 100 Shin- Chuan 1st Road, Kaohsiung 807, Taiwan.(email: [email protected]) Abstract An improved genetic algorithm (IGA) is presente to solve the mixed-discrete-continuous design optimization problems. The IGA approach combine the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select the better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The Use of Term Structure Information in the Hedging of Japanese Government Bonds Jian-Hsin Chou Associate Professor, Department of Risk Management and Insurance, National Kaohsiung First University of Science and Technology, Taiwan [email protected] Hong-Fwu Yu Professor,Department of Industrial Management, National Formosa University, Taiwan [email protected] Chien-Yun Chang Lecturer, Department of Finance Management, Hsiuping Institute of Technology, Taiwan [email protected] Abstract This paper employs the Kalman filter procedure to explore the impact of yield curve factors on the hedging of Japanese government bond (JGB) using treasury futures. Three parameters (i.e., level parameter, slope parameter, and curvature parameter) embedded in Nelson and Siegel (1987)[5] are used to be the proxies of interest rate risk. The out-of-sample hedging performance is also provided by moving window technology. The empirical results indicate that there is a significant relationship between the optimal hedge ratio and the yield curve factors. However, the time varying hedge ratio that includes the yield curve variables from the information set would not provide good out-of-sample hedging effectiveness. But the out-of-sample results also demonstrate that the performance of the time varying hedge ratio with yield curve factors is better than hedge ratio with naïve hedge or OLS model. Keyword: Kalman Filter,Yield Curve,Hedge Ratio. An Estimate of Deposit Insurance Premium Considering Liquidity Risk Min-Sun Horng, Liang-Fa Kao Department of Risk Management and Insurance, National Kaohsiung First University of Science and Technology, Taiwan [email protected] Roger C. Y. Chen Graduate Institute of Business Management National Kaohsiung First University of Science and Technology, Taiwan [email protected] Abstract The purpose of this article is to employ a deposit insurance pricing model to estimate the costs of deposit insurance considering liquidity risk. We use Monte Carlo simulation method for the premium evaluation and analysis. Our empirical results find that the premiums levied by the Central Deposit Insurance Corporation (CDIC) are lower for those listed institutions over the sample period. The technical insolvency does have its role in the deposit insurance premium. Even it is much lower than the cost of real insolvency. Keywords: technical insolvency, Monte Carlo simulation, bank run, deposit insurance Apply Genetic Algorithm to Explore Fund of Funds on Efficient Frontier and Product Design Abstract This paper employs Genetic Algorithm (GA）to construct the efficient frontier for the Fund of Funds (FoF) based on The Administration Acts of Security Investment and Trust Fund in Taiwan. The model proposed in this paper can be used to examine the effects of efficient frontier on different FoF and to verify the profit prediction power of alternative FoF constructed by GA. Our conclusions show that the current FoF based on The Administration Act of Security Investment and Trust Fund in Taiwan exhibits less efficiency. In particular, based on the optimal asset allocation principles of profit maximization and risk minimization, this paper employs three market indexes and fund’s asset net value as benchmark indexes for the constructions of FoF by applying GA. Compared to the performance of Taiwan Stock Exchange Corporation (TSEC) weighted price index, the average performance of domestic FoF and stock funds within the same period, the performance of the FoF constructed in this paper is more efficient across the test sample. In addition, this paper also shows that a FoF combined with the concepts of profit maximization and risk minimization as well as the order of Sharp index will offer a better and stable prediction power. Keywords: Fund of Funds, Genetic Algorithm, Efficient frontier A Nonmonotone MBFGS Method for Solving Nonconvex Minimization Problems Zheng Yue Huanggang Normal University Huanggang, 438000, China [email protected] Liu June Huanggang Normal University Huanggang, 438000, China [email protected] Abstract A nonmonotone MBFGS trust region method is presented for solving nonconvex minimization problems in the paper. Under mild conditions, the global convergence is proved. This may enlarge the applications of nonmonotone trust region methods. New Multi-Objective Constrained Optimization Evolutionary Algorithm Chun-an Liu Department of Mathematics Baoji University of Arts and Sciences Baoji, Shaanxi 721013, P. R. China [email protected] Abstract In this paper, a new evolutionary algorithm (EA) to solve multi-objective constrained optimization problem (MCOP) is proposed. First, the rank of the individual and the scalar constraint violation of the individual are defined. Then, based on the rank and the scalar constraint violation of the individual, a new fitness function and a switch selection operator are presented. Accordingly, when the individuals are evaluated or ranked, it doesn’t need to care about the feasibility of individuals, therefore it is a penalty-parameterless constraint-handling approach for multi-objective constrained optimization problem. Finally, the computer simulations demonstrate the effectiveness of the proposed algorithm. Precipitation state forecasting based on unascertained C-means and Markov chain model with gray relevancy weights Ma Li-hua, Yan Hui-zhe School of Economics and Management, Hebei University of Engineering, Handan, P.R.China, 056038 Email:[email protected] Abstract At present, in the field of hydrology and meteorological science, precipitation state forecasting is an extremely important problem. In this paper, the problem of precipitation state forecasting was studied, and a new forecasting method based unascertained c-means and Markov chain model with gray relevancy weights was presented. The method included the unascertained characteristic of precipitation state comprehensively, thus its forecasting outcomes are more scientific. Firstly, the unascertained C-means method is applied to divide time series of precipitation state, and the unascertained classification standard of precipitation state is established based on the fact that there are a lot of unascertained characteristics in the precipitation. Secondly, a forecasting
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