• 국제지역연구
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• 제12권2호
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• pp.3-26
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• 2008

본 논문은 호주 금융시장의 두 가지 시계열(ASX200 주가지수와 AUD/USD 환율)의 수익률 자료에 존재할 수 있는 장기기억 변동성 특성을 모형화하는 데 skewed Student-t 분포가 유용한지를 연구한다. 이러한 연구목적을 위하여 FIGARCH 및 FIAPARCH Value-at-Risk (VaR) 모형을 교란항에 대한 정규분포, Student-t 분포 및 치우친 Student-t 분포 가정하에서 평가한다. 실증분석 결과 skewed Student-t 분포 모형이 정규분포 모형이나 Student-t 분포 모형보다 호주 금융시장의 VaR을 더 정확하게 추정한다는 발견하였다. 따라서 자산 수익률 분포의 왜도 및 첨도를 고려하는 것은 호주 주식시장과 외환시장의 장기기억 변동성 모형을 검토할 때 적절한 모형선택 기준을 제공한다는 것을 알 수 있다.

• 자원ㆍ환경경제연구
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• 제16권4호
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• pp.947-978
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• 2007

본 연구에서는 원유시장의 변동성 분석에 적용될 수 있는 VaR(Value-at-Risk) 접근법을 고찰한다. 그리고 다양한 VaR 모형들(RiskMetrics, GARCH, IGARCH와 FIGARCH 모형)의 성과를 정규분포와 치우친 Student-t 분포 가정 하에서 평가한다. Brent 및 Dubai 시장의 일별가격 자료를 이용한 실증분석 결과에 따르면, FIGARCH 모형이 GARCH 모형이나 IGARCH 모형보다 원유시장의 변동성에 내재되어 있는 장기기억 특성을 잘 반영한다는 점에서 더 우월한 것으로 나타났다. 이러한 사실은 원유시장 수익률의 변동성에는 장기기억이 존재한다는 것을 의미한다. 그리고 VaR 분석 결과, 치우친 Student-t 분포 가정 하에서 추정되는 FIGARCH 모형이 롱 포지션과 숏 포지션 모두에서 정규분포 가정 하에서 추정되는 다른 변동성 모형들보다 원유시장에서의 투자 위험을 더 정확하게 예측하는 것으로 나타났다. 이러한 사실은 치우친 Student-t 분포 가정이 원유시장 수익률 분포에 내재되어 있는 비정상적 왜도와 첨도를 모형화하는데 더 적합하다는 것을 의미한다. 이와 같은 발견은 원유시장 구매자 및 판매자들이 원유가격의 움직임을 올바르게 측정하고 VaR을 정확하게 추정하는데 도움을 줄 것이다.

• 재무관리연구
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• 제24권3호
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• pp.153-186
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• 2007

It is well known that the distributional properties of financial asset returns exhibit fatter-tails and skewer-mean than the assumption of normal distribution. The correct assumption of return distribution might improve the estimated performance of the Value-at-Risk(VaR) models in financial markets. In this paper, we estimate and compare the VaR performance using the RiskMetrics, GARCH and FIGARCH models based on the normal and skewed-Student-t distributions in two daily returns of the Korean Composite Stock Index(KOSPI) and Korean Won-US Dollar(KRW-USD) exchange rate. We also perform the expected shortfall to assess the size of expected loss in terms of the estimation of the empirical failure rate. From the results of empirical VaR analysis, it is found that the presence of long memory in the volatility of sample returns is not an important in estimating an accurate VaR performance. However, it is more important to consider a model with skewed-Student-t distribution innovation in determining better VaR. In short, the appropriate assumption of return distribution provides more accurate VaR models for the portfolio managers and investors.

• Journal of the Korean Statistical Society
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• 제32권4호
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• pp.425-448
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• 2003

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution proposed originally by Chen et al. (1999) and Branco and Dey (2001). These rich classes of models combine the information of independent studies, allowing investigation of variability both between and within studies, and incorporate weight function. Here, the testing for the skewness parameter is discussed. The score test statistic for such a test can be shown to be expressed as the posterior expectations. Also, we consider the detail computational scheme under skewed normal and skewed Student-t distribution using MCMC method. Finally, we introduce one example from Johnson (1993)'s real data and apply our proposed methodology. We investigate sensitivity of our results under different skewed errors and under different prior distributions.

• 응용통계연구
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• 제30권5호
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• pp.775-791
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• 2017

이진 데이터는 일상 생활에서 자주 접할 수 있는 데이터이다. 이진 데이터를 회귀 분석하는 방법으로 로지스틱(Logistic), 프로빗(Probit), Cauchit, Complementary log-log 모형이 주로 쓰이는데, 이 방법 이외에도 Liu(2004)가 제시한 t 분포를 이용한 로빗(Robit) 모형, Kim 등 (2008)에서 제시한 일반화 t-link 모형을 이용한 방법 등이 있다. 유연한 분포를 이용하면 유연한 회귀 모형이 가능해지는 점에 착안하여, 이 논문에서는 Theodossiou(1998)에서 제시된 기운 일반화 t 분포 (Skewed Generalized t Distribution)의 이용하여 우도 함수를 최대로 하는 이진 데이터 회귀 모형을 소개한다. 기운 일반화 t 분포를 R glm 함수, R sgt 패키지를 연결하여 이 논문에서 제시한 방법을 R로 분석할 수 있는 방법을 소개하고, 피마 인디언(Pima Indian) 데이터를 분석한다.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• Interdisciplinary Bio Central
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• 제2권2호
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• pp.4.1-4.6
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• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

• Communications for Statistical Applications and Methods
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• 제21권5호
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• pp.445-459
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• 2014

This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJR-GARCH with skewed elliptical distributions and four copulas (Gaussian, Student's t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from $2^{nd}$ May 2006 to $30^{th}$ June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.