Jow-Ran Chang

The Jump Behavior of Foreign Exchange Market: Analysis of Thai Baht

We use square root stochastic volatility with or without jump model to study the heteroskedasticity and jump behavior of the Thai Baht. Bayesian factor is used to evaluate the explanatory power of competing model. It turns out that the square root stochastic volatility model with independent jump in observation and state equations (SVIJ) has the best explanatory power to our sample. Using the estimation results of the SVIJ model, we are able to link the major events of the Asian financial crisis to the jump behavior of either volatility or observation.

An International Asset Pricing Model with Time-Varying Hedging Risk

This paper employs a two-factor international equilibrium asset pricing model to examine the pricing relationships among the worlds five largest equity markets. In addition to the traditional market factor premium, a hedging factor premium is included as the second factor to explain the relationship between risks and returns in the international stock markets. Moreover, a GARCH parameterization is adopted to characterize the general dynamics of the conditional second moments. The results suggest that the additional hedging risk premium is needed to explain rates of return on international equities. Furthermore, the restriction that the coefficient on the hedge-portfolio covariance is one smaller than the coefficient on the market-portfolio covariance can not be rejected. This suggests that the intertemporal asset pricing model proposed by Campbell (1993) can be used to explain the returns on the five largest stock market indices.

Statistical Decision Theory

This chapter was originally in Lee et al. (2013). It discusses the statistical decision theory. The main topics covered in this chapter are (1) Four Key Elements of a Decision, (2) Decisions Based on Extreme Values, (3) Expected Monetary Value and Utility Analysis, (4) Bayes Strategies, (5) Decision Trees and Expected Monetary Values, (6) Mean and Variance Trade-Off Analysis (Optional), and (7) The Mean and Variance Method for Capital Budgeting Decisions.

Simulation and Its Application

In this chapter, we will introduce Monte Carlo simulation which is a problem-solving technique. This technique can approximate the probability of certain outcomes by using random variables, called simulations. Monte Carlo simulation is named after the city in Monaco. The primary attractions in this place are casinos having gambling games, like dice, roulette, and slot machines. In these games of chance, there exist random behavior.

Intertemporal hedge for inflation risk

An asset pricing model is developed in which price of consumption good is unknown and investors have a general time and state nonseparable preference. It is shown that the expected return on an asset is determined by a weighted average of market risk, inflation risk and consumption risk. The sum of these weights is equal to one. Moreover, a log-linear approximation proposed by Campbell (1993) to budget constraint is used to substitute out consumption to obtain an asset pricing model with inflation hedging risk. In this setup, expected asset return can be rewritten as a weighted average of market risk, market hedging risk, inflation risk and inflation hedging risk.

Other Continuous Distributions

We will now look at other continuous distributions that are commonly used in statistics. It is important to study all these distributions because later we will make statistical inferences based on the particular distribution we are working with. We should remember that the area under the density curve of each of these distributions is equal to 1.

Microsoft Excel Approach to Estimating Alternative Option Pricing Models

This chapter shows how Microsoft Excel can be used to estimate call and put options for (a) Black–Scholes model for individual stock, (b) Black–Scholes model for stock indices, and (c) Black–Scholes model for currencies. In addition, we are going to present how an Excel program can be used to estimate American Options. Section 26.2 presents an option pricing model for Individual Stocks, Sect. 26.3 presents an option pricing model for Stock Indices, Sect. 26.4 presents option pricing model for Currencies, Sect. 26.5 presents Bivariate Normal Distribution Approach to calculate American Call Options, Sect. 26.6 presents the Black’s approximation method to calculate American Call Options, Sect. 26.7 presents how to evaluate American Call option when dividend yield is known, and Sect. 26.8 summarizes this chapter. Appendix 26.1 defines the Bivariate Normal probability density function, and Appendix 26.2 presents the Excel program to calculate the American call option when dividend payments are known.

Portfolio Analysis and Option Strategies

The main purposes of this chapter are to show how Excel programs can be used to perform portfolio selection decisions and to construct option strategies. In Sect. 29.2, we demonstrate how Microsoft Excel can be used to inverse the matrix. In Sect. 29.3, we discuss how Excel programs can be used to estimate the Markowitz portfolio models. In Sect. 29.4, we discuss alternative option strategies. In Sect. 29.5, we summarize the results of this chapter.

Data Collection, Presentation, and Yahoo Finance

Statistics is mainly associated with numbers. A complete statistical analysis of a data set consists of both numbers and graphs. Graphs many times allow users to understand a data set that is very hard to capture with number.

Hedge Ratios: Theory and Applications

One of the best uses of derivative securities such as futures contracts is in hedging. In the past, both academicians and practitioners have shown great interest in the issue of hedging with futures. This is quite evident from the large number of articles written in this area.