International Journal of Theoretical and Applied Finance

We introduce a new approach for modeling the prepayments of a mortgage pool and show how it can be used to value mortgage pools and agency mortgage-backed securities. We describe the full spectrum of refinancing behavior using a notion of refinancing efficiency. Our approach has two distinguishing features: (1) our primary focus is on understanding the market value of a mortgage, in contrast with standard models that strive (often unsuccessfully) to predict future cash flows, and (2) we use two separate yield curves, one for modeling mortgage cash flows and the other for MBS cash flows.

In this paper we propose a forward time update algorithm to recursively estimate subset vector autoregressive models (including an intercept term) with a forgetting factor, using the exact window case. The proposed recursions cover, for the first time, subset vector autoregressive models (VAR) with a forgetting factor and an intercept variable. We then present two applications. In the first application we apply the proposed estimation algorithm to the quarterly aluminium prices on the London Metal Exchange. The findings show that the proposed algorithm can improve the forecasting performance. In the second application a bivariate system investigates the relationship between the Australian's All Ordinaries Share Price Index (SPI) futures and BHP share price (BHP). The proposed algorithm also introduces the Monte Carlo Integration approach into the proposed algorithm to generate error bands for the impulse responses. These results confirm that the SPI Granger causes BHP, but not vice versa.

In this paper we propose a continuous time model capable of describing the dynamics of futures equity index returns at different time frequencies. Unlike several related works in the literature, we avoid specifying a model a priori and we attempt, instead, to infer it from the analysis of a data set of 5-minute returns on the S&P500 futures contract. We start with a very general specification. First we model the seasonal pattern in intraday volatility. Once we correct for this component, we aggregate intraday data into a daily volatility measure to reduce the amount of noise and its distorting impact on the results. We then employ this measure to infer the structure of the stochastic volatility model and of the leverage component, as well as to obtain insights on the shape of the distribution of conditional returns. Our model is then refined at a high frequency level by means of a simple nonlinear filtering technique, which provides an intraday update of volatility and return density estimates on the basis of observed 5-minute returns. The results from a Monte Carlo experiment indicate that a sample of returns simulated according to our model successfully replicates the main features observed in market returns.

We investigate the portfolio construction problem for risk-averse investors seeking to minimize quantile based measures of risk. Using dependence measures from extreme value theory, we find that most international equity markets are asymptotically independent. We also find that the few cases of asymptotic dependence occur mostly in markets which are in close geographic proximity. We then examine how extremal dependence affects the asset allocation problem. Following the structure variable approach, we focus on the portfolio and model its tail in a manner consistent with extreme value theory. We then develop a methodology for asset allocation where the goal is to guard against catastrophic losses. The methodology is tested through simulations and applied to portfolios made up of two or more international equity markets. We analyze in detail three typical types of markets, one where the assets are asymptotically independent and the ratio of marginal risks is not constant, the second where the assets are asymptotically independent but the ratio of marginal risks are approximately constant and the third where the assets are asymptotically dependent and the ratio of marginal risks is not constant. The results are compared with the optimal portfolio under the assumption of normally distributed returns. Surprisingly, we find that the assumption of normality incurs only a modest amount of extra risk for all but the largest losses. We make the software written in support of this work freely available and describe its use in the appendix.

This paper employs a battery of statistical tests to examine the random walk variant of the weak-form efficient market hypothesis (EMH) using the daily data of the Dhaka Stock Exchange, the major equity market of Bangladesh, over a period of January 1990 to December 2000. The test results, however, are at variance across testing procedures and sub-periods. Results based on the random walk model and unit root tests show that the null hypothesis of randomness cannot be rejected and stock prices have a significant random walk or permanent component. Our analysis of autocorrelation functions indicates mean-reversion behavior of stock returns in most cases albeit with stock returns exhibiting some memory and predictable components during the bubble and post-speculation periods. The evaluation of the EGARCH-M model suggests significant asymmetric and leverage effects during the sub-period of speculative bubbles of 1996–1997. The BDS test indicates evidence of nonlinear long-term dependence during the pre-speculation period, while during the speculation and post-speculation periods the null hypothesis of nonlinear independence was not rejected. Overall, based on this evidence we do not categorically claim that the Dhaka Stock Exchange is weak-form efficient. However, these findings underscore the predictive significance and relevance of the random walk hypothesis as a generalized theory in explaining movements of share prices.