Paper # Author Title
Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-of change forecasts useful for market timing. We attempt to do so in an international sample of developed equity markets, with some success, as assessed by formal probability forecast scoring rules such as the Brier score. An important ingredient is our conditioning not only on conditional mean and variance information, but also conditional skewness and kurtosis information, when forming direction-of-change forecasts. Download Paper
This paper develops a model which is able to forecast exchange rate turmoil. Our starting point relies on the empirical evidence that exchange rate volatility is not constant. In fact, the modeling strategy adopted refers to the vast literature of the GARCH class of models, where the variance process is explicitly modeled. Further empirical evidence shows that it is possible to distinguish between two different regimes: "ordinary" versus "turbulence". Low exchange rate changes are associated with low volatility (ordinary regime) and high exchange rate devaluations go together with high volatility. This calls for a regime switching approach. In our model we also allow the transition probabilities to vary over time as functions of economic and financial indicators. We find that real effective exchange rate, money supply relative to reserves, stock index returns and bank stock index returns and volatility are the major indicators. Download Paper
Popular monthly coincident indices of business cycles, e.g., the composite index and the Stock-Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock-Watson coincident index by applying maximum likelihood factor analysis to a mixed-frequency series of quarterly real GDP and monthly coincident business cycle indicators. The resulting index is related to latent monthly real GDP. Download Paper
A Markov regime switching model for exchange rate fluctuations, with time-varying transition probabilities, is used in constructing a monthly model for predicting currency crises in Southeast Asia. The approach is designed to avoid the estimation inconsistency that might arise from misclassification errors in the construction of crisis dummy variables which other approaches (such as probit/logit and signaling) require. Our methodology also addresses the serial correlations and sudden behavior inherent in crisis occurrence, identifies a set of reliable and observable indicators of impending crisis difficulties, delivers forecast probabilities of future crises over multi-period forecasting horizons, and offers an empirical framework for analyzing contagion effects of a crisis. Our empirical results indicate that the Markov switching model is moderately successful at predicting crisis episodes, but also points to future research in various directions. Most early warning systems for currency crises have used either probit or signaling. Several issues can be raised regarding these techniques: the need for a priori dating of crisis occurrence, the use of arbitrary thresholds, inadequate modeling of the dynamics in the system, among others. We present an alternative framework, based on a Markov-switching model of exchange rate fluctuations with time-varying transition probabilities, which addresses these concerns. Download Paper
This paper investigates the feedback relationship between stock market returns and economic fundamentals in an emerging market. Starting from an intertemporal consumption-based CAPM (CCAPM), we obtain a restricted VAR model for stock returns and macroeconomic variables. We then apply this model to Korea and find statistically significant departures from the restrictions implied by CCAPM. Consequently, an unrestricted VAR model is used to analyze the variations of expected and unexpected returns in the Korean stock market. It is shown that the expected market returns vary with a set of macroeconomic variables, and that the predictable component is substantial. Reflecting richer dynamics in the data, relative to the usual single equation modeling in the literature, the estimated VAR model shows considerable predictive ability for both real economic activity and real returns. Using the model for a variance decomposition of unexpected returns, we find that, although we cannot directly observe the market's revision of expected future dividend growth, we can estimate a large part of the revision with the news in the expected industry output growth from our VAR model. Finally, we also find that economic fundamentals can explain only a small portion of the variation in unexpected returns in the Korean stock market. Copyright Kluwer Academic Publishers 1997 Download Paper
We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters. Download Paper