Abdul Abiad

Financial Reform: What Shakes It? What Shapes It?

What accounts for the worldwide advance of financial reforms in the last quarter century? Using a new index of financial liberalization, we find that influential events shook the policy status quo. Balance-of-payments crises spurred reforms, but banking crises set liberalization back. Falling global interest rates strengthened reformers, while new governments went both ways. The overall trend toward liberalization, however, reflected pressures and incentives generated by initial reforms that raised the likelihood of additional reforms, stimulated further by the need to catch up with regional reform leaders. In contrast, ideology and country structure had limited influence.

Early Warning Systems: A Survey and a Regime-Switching Approach

Previous early-warning systems (EWSs) for currency crises have relied on models that require a priori dating of crises. This paper proposes an alternative EWS, based on a Markov-switching model, which identifies and characterizes crisis periods endogenously; this also allows the model to utilize information contained in exchange rate dynamics. The model is estimated using data for the period 1972-99 for the Asian crisis countries, taking a country-by-country approach. The model outperforms standard EWSs, both in signaling crises and reducing false alarms. Two lessons emerge. First, accounting for the dynamics of exchange rates is important. Second, different indicators matter for different countries, suggesting that the assumption of parameter constancy underlying panel estimates of EWSs may contribute to poor performance.

The quality effect: Does financial liberalization improve the allocation of capital?

The study documents evidence of a quality effect of financial liberalization on allocative efficiency, which is measured by the dispersion in Tobins Q across firms. Based on a simple model, the authors predict that financial liberalization, by equalizing access to credit, reduces the variation in expected marginal returns. They test this prediction using a new financial liberalization index and firm-level data for five emerging markets: India, Jordan, Korea, Malaysia, and Thailand. They find strong evidence that financial liberalization, rather than financial deepening, improves allocative efficiency.

Markov Chains in Predictive Models of Currency Crises: With Applications to Southeast Asia

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.

Early Warning Systems for Currency Crises: A Regime-Switching Approach

Previous early warning systems (EWS) for currency crises have relied on models that require a priori dating of crises. This paper proposes an alternative EWS, based on a Markov-switching model, which identifies and characterizes crisis periods endogenously; this also allows the model to utilize information contained in exchange rate dynamics. The model is estimated on data from 1972–1999 for the Asian crisis countries, taking a country-by-country approach. The model outperforms standard EWSs, both in signaling crises and reducing false alarms. Two lessons emerge. First, accounting for the dynamics of exchange rates is important. Second, different indicators matter for different countries, suggesting that the assumption of parameter constancy underlying panel estimates of EWSs may contribute to poor performance.