International Journal of Information Technology & Decision Making

An electricity tolling agreement (or, tolling contact) is a supply contract in which the buyer reserves the right to take the output of an underlying electricity generation asset by paying a predetermined premium to the asset owner. We propose a real options approach to value a tolling contract incorporating operational characteristics of the generation asset and contractual constraints. Dynamic programming and value function approximation by Monte Carlo based least-squares regression are employed to solve the valuation problem. The effects of different electricity price assumptions on the valuation of tolling contracts are examined through numerical examples.

The main purpose of this study is to devise a general regression neural network (GRNN)-based currency crisis forecasting model for Southeast Asian economies based upon the disastrous 1997–1998 currency crisis experience. For this some typical indicators of currency exchange rates volatility are first chosen, then these indicators are input into GRNN for training, and finally the trained GRNN is used for future crisis prediction. To verify the effectiveness of the proposed currency crisis forecasting approach, four typical Southeast Asian currencies, Indonesian rupiah, Philippine peso, Singapore dollar and Thai baht, are selected. Meantime we compare its performance with those of other forecasting methods to evaluate the forecasting ability of the proposed approach. Empirical results obtained reveal that the proposed currency crisis forecasting model has a surprisingly high degree of accuracy in judging the currency crisis level of each country in specified time period, implying that our proposed approach can be used as a feasible currency crisis early-warning system to predict currency crisis level for other countries around the world.

BSV (Barberis, Shleifer and Vishny [Journal of Financial Economics49 (1998) 307–343]) model is one of the three major behavioral finance models. The existing BSV model is about how behavioral investors form beliefs, and is able to produce both overreaction and mean-reversion for a wide range of parameter values. However, the assumption that all investors in the market must all be BSV investors is a little strict and remains controversial. In this paper, we present an agent-based computational model of the dynamic game between BSV investors and rational investors. Time series from the artificial stock market are analyzed and two interesting findings are reported. First, the introduction of rational investors will not eliminate the anomalies of overreaction and mean-reversion. Second, no evidence is found that the BSV investors will lose money to their counterparts although their cognitive bias makes them form a false kind of expectation. On the contrary, some weak evidence is reported that BSV investors are less likely to bankrupt.

In this paper, we propose a framework to estimate the yield curve in the illiquid market. Within this framework, seven different curve-fitting models are compared from four aspects with the trading data of government bonds listed in the Shanghai Stock Exchange (SSE) of China. We find that the exponential spline model is optimal for this market. The characteristics and reasons underlying SSE interest rate fluctuations in the past two years are also analyzed.

Default correlation is the key point for the pricing of multi-name credit derivatives. In this paper, we apply copulas to characterize the dependence structure of defaults, determine the joint default distribution, and give the price for a specific kind of multi-name credit derivative — collateralized debt obligation (CDO). We also analyze two important factors influencing the pricing of multi-name credit derivatives, recovery rates and copula function. Finally, we apply Clayton copula, in a numerical example, to simulate default times taking specific underlying recovery rates and average recovery rates, then price the tranches of a given CDO and then analyze the results.

The notion of long-term memory has received considerable attention in empirical finance. This paper makes two main contributions. First one is, the paper provides evidence of long-term memory dynamics in the equity market of China. An analysis of market patterns in the Chinese market (a typical emerging market) instead of US market (a developed market) will be meaningful because little research on the behaviors of emerging markets has been carried out previously. Second one is, we present a comprehensive research on the long-term memory characteristics in the Chinese stock market returns as well as volatilities. While many empirical results have been obtained on the detection of long-term memory in returns series, very few investigations are focused on the market volatility, though the long-term dependence in volatility may lead to some types of volatility persistence as observed in financial markets and affect volatility forecasts and derivative pricing formulas. By means of using modified rescaled range analysis and Autoregressive Fractally Integrated Moving Average model testing, this study examines the long-term dependence in Chinese stock market returns and volatility. The results show that although the returns themselves contain little serial correlation, the variability of returns has significant long-term dependence. It would be beneficial to encompass long-term memory structure to assess the behavior of stock prices and to research on financial market theory.

This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.

Based on cluster analysis, a novel method is introduced in this paper to generate multistage scenarios. A linear programming model is proposed to exclude the arbitrage opportunity by appending a scenario to the generated scenario set. By means of a cited stochastic linear goal programming portfolio model, a case is given to exhibit the virtues of this scenario generation approach.

This paper deals with two bi-objective models arising from competitive location problems. The first model simultaneously intends to maximize market share and to minimize cost. The second one aims to maximize both profit and the profit margin. We study some of the related properties of the models, examine relations between the models and a single objective parametric integer programming problem, and then show how both bi-objective location problems can be solved through the use of a single objective parametric integer program. Based on this, we propose two methods of obtaining a set of efficient solutions to the problems of fundamental approach. Finally, a numerical example is presented to illustrate the solution techniques.

We present a stochastic optimization model for hotel revenue management with multiple-day stays under an uncertain environment. Since a decision maker may face several scenarios when renting out rooms, we use a semi-absolute deviation model to measure the risk of hotel revenue, and only consider the risk of falling below the expected revenue. The method proposed in this paper can be changed to a linear programming model by applying linearization techniques. Some examples are presented to illustrate the efficiency of this method.

We study two kinds of buy-it-now options, temporary and permanent, under a theoretical model of Stackelberg game. In this two-stage game, the bidders, as the followers, use a two-threshold strategy to determine whether to bid or directly buy the item at the posted price, given an auction configuration featured by the seller in the first stage and other common knowledge. Under the uniform distribution assumption for the bidders' valuation, we derive the optimal necessary conditions of the starting price and the buy-it-now price for maximizing the seller's expected revenue. Then, we use two numerical experiments to find some interesting insights, which include that under identical bidders' participation costs, the temporary buy-it-now option can acquire a higher expected revenue for the seller than the permanent option, and a buy-it-now price auction always nontrivially dominates a regular auction in terms of the achieved expected revenue, no matter whether the seller or the bidders are risk-averse.