• Title/Summary/Keyword: 비대칭 라플라스 분포

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Validity assessment of VaR with Laplacian distribution (라플라스 분포 기반의 VaR 측정 방법의 적정성 평가)

  • Byun, Bu-Guen;Yoo, Do-Sik;Lim, Jongtae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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    • pp.1263-1274
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    • 2013
  • VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

Modeling sharply peaked asymmetric multi-modal circular data using wrapped Laplace mixture (겹친라플라스 혼합분포를 통한 첨 다봉형 비대칭 원형자료의 모형화)

  • Na, Jong-Hwa;Jang, Young-Mi
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.5
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    • pp.863-871
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    • 2010
  • Until now, many studies related circular data are carried out, but the focuses are mainly on mildly peaked symmetric or asymmetric cases. In this paper we studied a modeling process for sharply peaked asymmetric circular data. By using wrapped Laplace, which was firstly introduced by Jammalamadaka and Kozbowski (2003), and its mixture distributions, we considered the model fitting problem of multi-modal circular data as well as unimodal one. In particular we suggested EM algorithm to find ML estimates of the mixture of wrapped Laplace distributions. Simulation results showed that the suggested EM algorithm is very accurate and useful.

Quantile regression using asymmetric Laplace distribution (비대칭 라플라스 분포를 이용한 분위수 회귀)

  • Park, Hye-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.6
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    • pp.1093-1101
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    • 2009
  • Quantile regression has become a more widely used technique to describe the distribution of a response variable given a set of explanatory variables. This paper proposes a novel modelfor quantile regression using doubly penalized kernel machine with support vector machine iteratively reweighted least squares (SVM-IRWLS). To make inference about the shape of a population distribution, the widely popularregression, would be inadequate, if the distribution is not approximately Gaussian. We present a likelihood-based approach to the estimation of the regression quantiles that uses the asymmetric Laplace density.

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Bayesian inference on multivariate asymmetric jump-diffusion models (다변량 비대칭 라플라스 점프확산 모형의 베이지안 추론)

  • Lee, Youngeun;Park, Taeyoung
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.99-112
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    • 2016
  • Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.