Sung Y. Park

Optimal Portfolio Diversification Using the Maximum Entropy Principle

Markowitzs mean-variance (MV) efficient portfolio selection is one of the most widely used approaches in solving portfolio diversification problem. However, contrary to the notion of diversification, MV approach often leads to portfolios highly concentrated on a few assets. Also, this method leads to poor out-of-sample performances. Entropy is a well-known measure of diversity and also has a shrinkage interpretation. In this article, we propose to use cross- entropy measure as the objective function with side conditions coming from the mean and variance–covariance matrix of the resampled asset returns. This automatically captures the degree of imprecision of input estimates. Our approach can be viewed as a shrinkage estimation of portfolio weights (probabilities) which are shrunk towards the predetermined portfolio, for example, equally weighted portfolio or minimum variance portfolio. Our procedure is illustrated with an application to the international equity indexes.

Interrelationships among Korean Outbound Tourism Demand: Granger Causality Analysis

This study investigates Korean outbound tourism demand and its determinants using the Granger causality (GC) analysis. In contrast to previous studies, which deal only with internal factors such as exchange rate and income, this study examines the effects of interactions among countries and, therefore, produces more complete and relevant results. Korean outbound tourism to the USA is causally related to Korean outbound tourism to the other six countries in the study. These results can be used in tourism marketing and strategic planning by industry and government to allocate tourism resources more efficiently.

Testing for a unit root in a nonlinear quantile autoregression framework

ABSTRACT The nonlinear unit root test of Kapetanios, Shin, and Snell (2003) (KSS) has attracted much recent attention. However, the KSS test relies on the ordinary least squares (OLS) estimator, which is not robust to a heavy-tailed distribution and, in practice, the test suffers from a large power loss. This study develops three kinds of quantile nonlinear unit root tests: the quantile t-ratio test; the quantile Kolmogorov–Smirnov test; and the quantile Cramer–von Mises test. A Monte Carlo simulation shows that these tests have significantly better power when an innovation follows a non-normal distribution. In addition, the quantile t-ratio test can reveal the heterogeneity of the asymmetric dynamics in a time series. In our empirical studies, we investigate the unit root properties of U.S. macroeconomic time series and the real effective exchange rates for 61 countries. The results show that our proposed tests reject the unit roots more often, indicating that the series are likely to be asymmetric nonlinear reverting processes.

Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression

Abstract This paper studies the connections among the asymmetric Laplace probability density (ALPD), maximum likelihood, maximum entropy and quantile regression. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. The ALPD score functions lead to joint estimating equations that delivers estimates for the slope parameters together with a representative quantile. Asymptotic properties of the estimator are derived under the framework of the quasi maximum likelihood estimation. With a limited simulation experiment we evaluate the finite sample properties of our estimator. Finally, we illustrate the use of the estimator with an application to the US wage data to evaluate the effect of training on wages.

Quantile elasticity of international tourism demand for South Korea using the quantile autoregressive distributed lag model

Using the quantile autoregressive model, this paper investigates international inbound tourism demand for South Korea and its determinants. In contrast to previous studies which have dealt with the conditional mean only, the authors examine the effects of covariates at various conditional quantile levels. US and Japanese tourism demand are considered for inbound tourism demand. For US tourism demand, the costs of living in Korea and competing destinations have moderately significant negative effects at very high and low quantiles only, while income does not have any significant effect on tourism demand. On the other hand, for Japanese tourism demand, income has significant positive effects at lower quantiles, and living costs in Korea and competing destinations have significant negative effects at higher quantiles. These results address the heterogeneity in tourism demand analysis.

Which quantile is the most informative? Maximum likelihood, maximum entropy and quantile regression

This paper studies the connections among quantile regression, the asymmetric Laplace distribution, maximum likelihood and maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we propose an estimator based on the joint estimating equations. This approach delivers estimates for the slope parameters together with the associated “most probable” quantile. Similarly, this method can be seen as a penalized quantile regression estimator, where the penalty is given by deviations from the median regression. We derive the asymptotic properties of this estimator by showing consistency and asymptotic normality under certain regularity conditions. Finally, we illustrate the use of the estimator with a simple application to the U.S. wage data to evaluate the effect of training on wages.

Determinants of systematic risk in the US Restaurant industry A technical perspective

To compare previous studies, this study re-examines the determinants of systematic risk in the restaurant industry. To estimate systematic risk, the authors specify flexible models that take care of serial dependence, autoregressive conditional heteroskedasticity and non-normality of the time series data. Using the estimated systematic risk, they analyse the determinants of risk using a quantile regression approach. The empirical results show that a firm’s liquidity ratio, efficiency ratio, debt leverage ratio and size are the main determinants of systematic risk in the restaurant industry. Moreover, it turns out that the effects of liquidity, debt leverage and efficiency decrease as the considered risk levels increase.

An empirical test for Okun’s law using a smooth time-varying parameter approach: evidence from East Asian countries

This article investigates Okun’s law for Japan, Korea, Hong Kong and Singapore over the period 1986–2011. Two time-varying parameter models, first-order difference and gap models, are considered to find a negative time-varying relationship between the real output and the unemployment rate. The empirical findings show that there exist time-varying negative relationships between the real output and the unemployment rate for all economies. We also find that the estimated time-varying Okun’s coefficients are dominated by changes in the real GDP for Korea. However, the Okun’s coefficients are dominated by changes in the unemployment rate for Japan, Hong Kong and Singapore.

Tourism Development and Economic Growth in Korea: Causal Relationship in Tails

Many empirical studies have investigated the existence of a causal relationship between a countrys tourism growth and economic growth. However, the findings from these studies have been inconclusive. Some studies have found evidence of a unidirectional causal relationship, whereas others have found a bidirectional causal relationship. This inconsistency may be due to the usage of different frequencies of data or an incomplete description of the causal relationship. This study examines the causal relationship between economic growth and tourism growth in Korea using three types of Granger noncausality tests: the classical Granger noncausality test, a robust Granger noncausality test, and a Granger noncausality test in quantiles. Our empirical results provide evidence of what appears to be a bidirectional causal relationship between tourism growth and economic growth in overall quantile intervals. There is strong support that tourism growth leads to overall economic growth in Korea. However, in the reverse relationship, economic growth only has a significant effect on tourism at low quantile levels of tourism growth. These findings suggest that the causal relationship is heterogeneous and depends on different levels of tourism growth and economic growth