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RISS 인기검색어
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- 저자 : Beum-Jo (검색결과 55 건)
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ARCH모형 : 확장, 추정 및 검정 Extensions, Estimation and Testing
박범조 단국대학교 경영경제연구소 1997 經營, 經濟硏究 Vol.1 No.-
The purpose of this survey paper is to provide a brief account of the important theoretical developments in the autoregressive conditional heteroskedasticity (ARCH) models. This paper begins with the specification of univariate parametric ARCH models and the extensions of ARCH models such as generalized ARCH, ARCH in mean and exponential ARCH. In addition, motivated in part by recent results on kernel estimation, a nonparametric conditional variance model is also presented. Next this paper discusses estimation and testing for ARCH models.
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Beum-Jo Park,Hong Chong Cho 한국재무학회 2014 한국재무학회 학술대회 Vol.2014 No.05
We propose a new eliciting method of measuring risk aversion through a laboratory experiment to overcome disadvantages of the multiple pricing list format developed by Holt and Laury (2002) and standardize the risk aversion ranking by quantile normalization. Our method doesn't stick to any specific utility function, and is free of the framing effect or the multiple switching problem. Furthermore, with the new measure of risk aversion, we examine how individuals change risk attitude and decision making time when they face new informational disadvantages, i.e., less information about asset markets than experts. Decision making time gets shorter and risk aversion rises significantly when individuals perceive themselves informationally disadvantaged.
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A Noise-Reduced Risk Aversion Index
Beum-Jo Park,Hong Chong Cho 한국데이타베이스학회 2018 Journal of information technology applications & m Vol.25 No.1
We propose a noise reduced risk aversion index for measuring risk aversion through a laboratory experiment to overcome disadvantages of the multiple pricing list format developed by Holt and Laury (2002). We use randomized multiple list choices with coarser classification and reward weighting, supplement the rank of risk aversion with extra individual characteristics of risk attitude, and construct an index of risk aversion by standardizing the risk aversion ranking with quantile normalization. Our method reduces multiple switching problems that noisy decision makers mistakenly commit in experimental approaches, so that it is free of the framing effect which severely occurred in the HL. Furthermore, the index doesn`t utilize any specific utility function or probability weighting, which allows researcher to hold the independence axiom. Since our noise reduced index of risk aversion has many good traits, it is widely used and applied to reveal fundamental characteristics of risk-related behaviors in economics and finance regardless of experimental environment.
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Herd behavior and volatility in financial markets
Beum Jo Park 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.6
Relaxing an unrealistic assumption of a representative pe-rcolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information ows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for de-tecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in nancial markets. Strong ev-idence in favor of the model specification over the standard stochastic volatility models is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addi-tion, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.
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박범조(Beum Jo Park) 한국증권학회 2010 한국증권학회지 Vol.39 No.1
금융시장에 특별한 뉴스(공적 정보)가 없음에도 불구하고 수익률의 변동성이 증폭되는 현상을 설명하기 위해 최근 무리행동에 대한 연구가 심도 있게 진행되고 있다. 하지만 무리행동발생의 계량적 측정이 쉽지 않기 때문에 무리행동이 변동성에 미치는 영향에 대한 경험적 연구는 거의 이루어지고 있지 못하다. 따라서 본 연구는 기본적 분석가와 잡음 거래자가 존재하는 금융시장의 2시점 모형을 통해 무리행동은 정보 유입과는 달리 거래량에 부의 영향을 미치게 됨을 이론적으로 입증하고, 점프 검정통계량과 분위수 평활 스플라인(quantile smoothing spline)을 응용하여 무리행동의 발생 시점을 탐지할 수 있는 방법을 새롭게 제안한다. 또한 무리행동의 존재 여부에 따라 변동성의 상태가 전환되는 확률변동성 모형을 개발하고, 이 모형의 효율적 추정을 위해 마코프 체인 몬테칼로(MCMC) 추정법을 적용한다. KOSPI 고빈도자료를 이용한 실증분석 결과에 의하면 무리행동을 반영한 확률변동성 모형이 다른 확률변동성 모형들에 비해 예상처럼 높은 적합성을 갖는다. 또한 무리행동의 발생은 수익률의 변동성을 확대하였으며, 무리행동을 반영한 확률변동성 모형에서 변동성의 지속성이 현저히 감소하였다. 이런 흥미로운 결과는 변동성의 지속성과 무리행동이 밀접한 관계가 있음을 의미한다. This paper examines the effect of herd behavior on volatility, which has been a growing issue in financial economics since the global financial crisis started in mid-2007. Using a simple two-period model with fundamentalists and noise traders, this paper theoretically demonstrates that, in contrast with information flows, herd behavior is likely to lead to a decrease in trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding based upon the theoretical result, jump test statistic, and quantile smoothing spline. Furthermore, this paper develops a stochastic volatility model, which accounts for not only herding but news in financial markets, and considers a Markov chain Monte Carlo method as an efficient method for estimating the model. Strong evidence in favor of the model specification over the other competitive stochastic volatility models is based on empirical application with high frequency data of KOSPI. This empirical result strongly supports the intuition that volatility is closely related to herd behavior. More interesting finding is that strong persistence in volatility, which is a prevalent feature in financial markets, tends to be reduced by consideration of herding in stochastic volatility models.
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