Tung Liu

Stock market volatility and regime shifts in returns

Abstract This paper relates variation in stock market volatility to regime shifts in stock market returns. We apply a Markov switching model to market returns and examine the variation in volatility in different return regimes. We find that stock returns are best characterized by a model containing six regimes with significantly different volatility across the regimes. Volatility is higher when returns are either above or below the normal regime—the further returns deviate from the normal regime, the higher the volatility. Furthermore, volatility is higher in negative return regimes than in positive return regimes. These observations lead us to conclude that return and volatility are related nonlinearly and that the relationship is asymmetric.

Performance of Financial Resources in China's Provinces

This paper examines the economic performance of financial resources in Chinas provinces for the period 1985-98. The empirical results indicate that different financial resources have different impacts on the economic growth. The growth of national bank loans and self-raised funds are important to the growth of provincial output. When a division is made between inner and coastal regions, diversion of financial resources has a significant impact on the economic growth of the coastal provinces, but not on the economic growth of interior provinces.

Law and Statistical Disorder: Statistical Hypothesis Test Procedures and the Criminal Trial Analogy

Virtually all business and economics statistics texts start their discussion of hypothesis tests with some more-or-less detailed reference to criminal trials. Apparently, these authors believe that students are better able to understand the relevance and usefulness of hypothesis test procedures by introducing them first via the dramatic analogy of the criminal justice system. In this paper, we argue that using the criminal trial analogy to motivate and introduce hypothesis test procedures represents bad statistics and bad pedagogy. First, we show that statistical hypothesis test procedures can not be applied to criminal trials. Thus, the criminal trial analogy is invalid. Second, we propose that students can better understand the simplicity and validity of statistical hypothesis test procedures if these procedures are carefully contrasted with the difficulties of decisionmaking in the context of criminal trials. The criminal trial discussion provides a bad analogy but an excellent counter-example for teaching statistical hypothesis procedures and the nature of statistical decision-making.