In this paper, we investigate detecting single change point under time series regression model with GARCH errors using the cumulative sum of squares of the least squares residuals test and the log-likelihood ratio test. Furthermore we think it is important to calculate confidence interval for an estimated change point, for which we need to know the sampling distribution of the estimated change point. We obtain the sampling distribution to calculate confidence interval using Monte Carlo simulation based on a circular block bootstrap method and verify the performance of the above break point tests by Monte Carlo experiment. Then we detect a change point in the exchange rate of Indonesian Rupiah (IDR) using the above test to detect. The Government of Indonesia officially announced (de jure) to adopt a floating exchange rate regime in August 1997. However, from time to time, Bank Indonesia nevertheless maintains the stability of rupiah value in the market. Since there is no official information regarding on central bank’s intervention in the foreign exchange market, therefore detecting a structural change in the time series of the exchange market can be used as an indicator of exchange rate management. Our real data analysis shows that the IDR had been moving with the USD since 2000, but that the direction of the relationship changed in March 2002. This indicates that there was some control over the Rupiah’s movement.

An efficient method for valuing credit derivatives based on three entities is developed in an affine framework. This includes interdependence of market and credit risk, joint credit migration and counterparty default risk of three firms. As an application we provide closed form expressions for the joint distribution of default times, default correlations, and default swap spreads in the presence of counterparty default risk.

Option pricing under the Lévy process has been considered an important research direction in the field of financial engineering, where a closed-form expression for the standard European option is available due to the existence of analytically tractable characteristic function according to the Lévy–Khinchin representation. However, this approach cannot be applied to exotic derivatives (such as barrier options) directly, although a large volume of exotic derivatives are actively traded in the current options market. An alternative approach is to solve the corresponding partial integro-differential equation (PIDE) numerically, which is, in fact, time-consuming and is not computationally tractable in general. In this paper, we apply the so-called homotopy analysis method (HAM) to solve the corresponding PIDE in a semi analytic form, being obtained from the following three steps: (1) Apply the Fourier transform to convert the PIDE to an ordinal differential equitation (ODE), and construct a differential system of ODEs. (2) Solve the system of ODEs, where each differential equation is shown to have an analytical solution. (3) Express the option price using the sum of infinite series, where each term may be expressed analytically and derived by applying Steps (1) and (2) recursively. To illustrate our technique more precisely, we take the variance gamma model as an example and provide the semi-analytic form. Numerical examples demonstrate a fast convergence of our proposed method to the prices of European and down-and-out call options with a few number of terms. Note that this method is easy to implement and can be applied to other types of options under general Lévy processes.

We propose a new method forfailure discrimination and rating of enterprises using financialdata compiled from their balance sheets. No particular distributional assumption is made on the underlying data. Our method automatically discriminates and rates many enterprises using mathematical programming methods. We separate multi-dimensional data byhyperplane and hyper-ellipsoid, so that we can interpretthe results of classification from the geometric point of view. Theproblem to be solved here is a linear programming problem orsemi-definite programming problem which can be solved efficiently byinterior point algorithms. Numerical simulations usingreal data show that hyper-ellipsoid separation generates a result which can be used for practical purposes.

In this article, we will consider a multi-dimensional geometric L'evy process as a financial market model. We will first determine the minimal entropy martingale measure (MEMM); we will next derive the optimal strategy for the exponential utility maximization of terminal wealth concretely from the representation of the MEMM.

This paper examines the effects of news surprises of macroeconomic announcements on Australian financial markets across different business cycles. We find that overall, the news arrivals are influential in both stock and debt markets but in an interesting array of responses across asset classes. Debt markets are more responsive to macroeconomic news surprises compared to the stock market, hence supporting the notion that information revealed from the macroeconomic news is related to interest rates. Specifically, news about CPI is important over the full sample period and especially during expansions for both stock and bond returns while the unemployment rate news is influential to the money market rates. Furthermore, these effects are seemingly asymmetric in nature, with their directions and magnitudes conditional on the state of economy.

Size effect studies generally suggest that a return premium exists for small firms. While the size effect has mostly disappeared in recent years in mature markets (e.g., US and UK), it remains mostly strong in developing markets. The purpose of this paper is to examine the relationship between firm size and excess stock returns in the Chinese stock markets, and to examine this effect in both a bull and bear market. No studies have previously examined these relationships in the Chinese markets. The results of the study indicate that a size effect exists in the Chinese stock markets over the 6-year period from 1998 to 2003. We find small firms have significantly greater excess returns than large firms. Moreover, small firms are found to have a stronger reaction to the direction of the market than large firms. Small firms have significantly greater positive excess returns than large firms during the bull market. However, small firms have significantly greater negative returns (using total market value), or no significant difference in returns (using float market value) during the bear market period.

We investigate whether the activity of financial firms creates value and/or risk to the economy within the asset pricing framework. We use stock return data from nonfinancial firms listed in the first section of the Tokyo Stock Exchange. The value-weighted index that is solely composed of nonfinancial firms is augmented with the index of the firms from the financial sector, and we estimate multivariate asset pricing model with these two indices. We note that our procedure can simultaneously take into account the cross-holding phenomena among Japanese firms, especially between the financial sector and the nonfinancial sector. Our augmented index model performs well both with cross-sectional Fama and MacBeth regression test and GMM test. Our two index model with additional Fama and French's HML factor can capture cross-sectional variations of the returns of sample portfolios better than the original Fama and French model can, when measured by Hansen and Jagannathan distance measure. We find that this additional new sector variable can be a substitute for Fama and French's size factor, but not related to the bond index return. This variable has similar factor characteristic as money supply growth or the term structure, but the latter variables contain more information than the former. Morever, our financial sector model helps explain the return and risk structure of Japanese firms during the so-called “bubble” period.

The hedging effectiveness for bank futures and CNX nifty are evaluated in this study. The study is based on 9,569 observations of the daily data for these index futures. For evaluation ordinary least square, co-integrated ordinary least square, generalized auto-regressive conditional heteroscedasticity (1, 1), and constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) hedging methods are estimated and compared. Result shows that constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) is an efficient hedging method that maximizes investors’ utility function considering transaction costs. Therefore, investors can rely on this constant correlation generalized auto-regressive conditional heteroscedasticity (1, 1) hedging method.

China has become recognized a fourth world economy and is playing a much more important role than ever before in the world economy. In this paper, we study the relationship between the China and the international main stock markets, including the stock markets in the U.S., the U.K., Japan and Hong Kong. Both long-term and short-term dynamic linkages between the China and the international main stock markets are explored by applying a Markov-Switching Vector Error Correction Model (MS-VECM), which takes into account the three regimes of depression, boom and speculation in the market. Our new findings with the data under study include: (i) There has been a significant trend of long-term co-movement between the China and the international stock markets since 1999. (ii) In short term, the stock market in China has been impacted directly or indirectly by the international main stock markets, which varies under different regimes. This impact is still weak in the depression regime, but strong in the boom regime, and, in particular, it has become very strong through the co-integration error correction in the regime of speculation. These findings are different from those documented in the literature and are potentially interesting for international investment and risk management.