Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity

We propose a nonparametric Bayesian approach to estimate time‐varying grouped patterns of heterogeneity in linear panel data models. Unlike the classical approach in Bonhomme and Manresa (Econometrica, 2015, 83, 1147–1184), our approach can accommodate selection of the optimal number of groups and model estimation jointly, and also be readily extended to quantify uncertainties in the estimated group structure. Our proposed approach performs well in Monte Carlo simulations. Using our approach, we successfully replicate the estimated relationship between income and democracy in Bonhomme and Manresa and the group characteristics when we use the same number of groups. Furthermore, we find that the optimal number of groups could depend on model specifications on heteroskedasticity and discuss ways to choose models in practice.     

A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back‐propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty‐two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back‐propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty‐two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons.     

DUALISM IN JOB STABILITY OF THE KOREAN LABOUR MARKET: THE IMPACT OF THE 1997 FINANCIAL CRISIS

Abstract.  This paper measures the dynamic changes in job stability in the Korean labour market during the period between the 1997 financial crisis and the recovery. Drawing on the Korean Labor and Income Panel Study data, our empirical research shows that the sharp decline in job stability experienced by Korean workers during the 1997 financial crisis never bounced back to the previous level. The recovery process was much slower for irregular, short-tenured or less educated workers than the other comparison groups, aggravating the polarization of job stability.     

The role of time-varying jump risk premia in pricing stock index options

This paper examines out-of-sample option pricing performances for the affine jump diffusion (AJD) models by using the S&P 500 stock index and its associated option contracts. In particular, we investigate the role of time-varying jump risk premia in the AJD specifications. Our empirical analysis shows strong evidence in favor of time-varying jump risk premia in pricing cross-sectional options. We also find that, during a period of low volatility, the role of jump risk premia becomes less pronounced, making the differences across pricing performances of the AJD models not as substantial as during a period of high volatility. This finding can possibly explain poor pricing perfomances of the sophisticated AJD models in some previous studies whose sample periods can be characterized by low volatility.     

The Korean stock market volatility during the currency crisis and the credit crisis

This paper studies the volatility of the Korean stock market during the Asian currency crisis of 1997–1998 and the global credit crisis of 2008–2009. We use a fad model with Markov switching heteroskedasticity, which was first proposed by Kim and Kim (1996). Using the monthly data from January 1980 to October 2009, we find that the volatility of the transitory component of the stock return, or fads, increased during the currency crisis, but did not rise much during the credit crisis. It implies that the stock price fluctuations were not driven by irrational sentiments during the recent global crisis as much as during the former crisis. However, when we consider the dollar value of the Korean stock index in order to estimate the volatility that foreign investors confront, we find that the volatility of the transitory component was raised during the credit crisis as well as during the currency crisis. That is, foreign investors experienced greater volatility than domestic investors in the recent financial market turmoil. This asymmetric volatility that domestic and foreign investors face is one of the characteristics of the credit crisis.For more detailed analysis, the same model was applied to the weekly data from January 2005 to October 2009 and provided the result that the data measured by won–dollar exchange rates were more increased than the raw data. It holds that foreign investors confronted much greater volatility than domestic investors while the stock volatility was relatively lower in the credit crisis state than in the currency crisis state.     

Risk aversion in the expected and the nonexpected utility functions

If asset returns are i.i.d. over time, the preference parameter in the time additive von Neumann-Morgenstern expected utility is the risk aversion coefficient in the Epstein-Zin nonexpected utility. By distinguishing between risk aversion and intertemporal substitution, this article provides an explanation about the observed discrepancy in the empirical estimates of the risk aversion coefficient.