Lean Yu

Shouyang Wang

K. K. Lai

Intelligent Computational Methods for Financial Engineering

The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility&#39;s forecasting. By using real data from S&#38;P500 index options, the genetic programming&#39;s ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting&#39;s performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

Wafa Abdelmalek

Sana Ben Hamida

Fathi Abid

Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decision-makers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2-tuple linguistic variable are suitable used to express experts&#39; opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decision-making method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.

Chen-Tung Chen

Wei-Zhan Hung

A New Decision-Making Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment

Real options modeling, which extends the ability of option pricing models to evaluate real assets, can
be used to evaluate risky projects because of its capacity to handle uncertainties. This research utilizes
possibility theory to represent private risks of a project, which are not reflected in the market and hence
are not fully evaluated by standard option pricing models. Using a transformation method, these private
risks can be represented as fuzzy variables and then priced with a fuzzy real options model. This principle
is demonstrated by valuing a brownfield redevelopment project using a prototype decision support
system based on fuzzy real options. Because they generalize the original model and enable it to deal
with additional uncertainties, fuzzy real options are entirely suitable for the evaluation of such projects.

Qian Wang

Keith W. Hipel

D. Marc Kilgour

Fuzzy Real Options in Brownfield Redevelopment Evaluation

This paper proposes a more comprehensive look at the ideas of KS and Area Under the Curve (AUC) of a cumulative
gains chart to develop a model quality statistic which can be used agnostically to evaluate the quality of a wide range of
models in a standardized fashion. It can be either used holistically on the entire range of the model or at a given decision
threshold of the model. Further it can be extended into the model learning process.

Thomas Brandenburger

Alfred Furth

Cumulative Gains Model Quality Metric

Real option analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.

Mikael Collan

Robert Fullér

József Mezei

A Fuzzy Pay-Off Method for Real Option Valuation

The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is imbalanced. The modified network algorithms are based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLSs) error function. An adjustment relating to the contribution from the historical data used for training the networks and penalisation of incorrectly classified trading signals were accounted for, when modifying the OLS function. A global optimization algorithm was employed to train these networks. These algorithms were employed to predict the trading signals of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.

C. D. Tilakaratne

M. A. Mammadov

S. A. Morris

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

The valuation for an American continuous-installment put option on zero-coupon bond is considered by Kim's equations under a single factor model of the short-term interest rate, which follows the famous Vasicek model. In term
of the price of this option, integral representations of both the optimal stopping and exercise boundaries are derived. A numerical method is used to approximate the optimal stopping and exercise boundaries by quadrature formulas. Numerical results and discussions are provided.

Guoan Huang

Guohe Deng

Lihong Huang

Valuation for an American Continuous-Installment Put Option on  Bond under Vasicek Interest  Rate Model

Classification of whether recovery of non-performing loans (NPL) is zero or positive is not only important in management of non-performing loans, but also is essential for estimating recovery rate and implementing the new Basel Capital Accord. Based on the largest database of NPL's recovering information in China, this paper tries to establish discriminant models to predict the loan with zero recovery. We first use Step-wise discrimination method to select variables; then give an in-depth analysis on why the selected variables are important factors influencing whether a loan is zero or positive recovery rate. Using the selected variables, we establish two-type discriminant models to classify the NPLs. Empirical results show that both models achieve high prediction accuracy, and the characteristics of obligors are the most important factors in determining whether a NPL is positively recovered or zero recovered.

Yue Tang

Hao Chen

Bo Wang

Muzi Chen

Min Chen

Xiaoguang Yang

Discriminant Analysis of Zero Recovery for China's NPL

We deal with the pricing of callable Russian options. A callable Russian option
is a contract in which both of the seller and the buyer have the rights to cancel and to exercise at any time,
respectively. The pricing of such an option can be formulated as an optimal stopping problem between the
seller and the buyer, and is analyzed as Dynkin game. We derive the value function of callable Russian
options and their optimal boundaries.

Atsuo Suzuki

Katsushige Sawaki

Callable Russian Options and Their Optimal Boundaries

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