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Confidence Intervals for the Stress-strength Models with Explanatory Variables
Lee, Sangyeol,Park, Eunsik The Korean Statistical Society 1998 Journal of the Korean Statistical Society Vol.27 No.4
In this paper, we consider the problem of constructing the lower cofidence intervals for the reliability P(X < Y z,w), where the stress X and the strength Y are the random variables with explanatory variables z and w, respectively. As an estimator of the reliability, a Mann-Whitney type statistic is considered. It is shown that under regularity conditions, the proposed estimator is asymptotically normal. Based on the result, the distribution free lower confidence intervals are constructed.
Dependence structure analysis of KOSPI and NYSE based on time-varying copula models
Lee, Sangyeol,Kim, Byungsoo The Korean Data and Information Science Society 2013 한국데이터정보과학회지 Vol.24 No.6
In this study, we analyze the dependence structure of KOSPI and NYSE indices based on a two-step estimation procedure. In the rst step, we adopt ARMA-GARCH models with Gaussian mixture innovations for marginal processes. In the second step, time-varying copula parameters are estimated. By using these, we measure the dependence between the two returns with Kendall's tau and Spearman's rho. The two dependence measures for various copulas are illustrated.
Forecasting value-at-risk by encompassing CAViaR models via information criteria
Lee, Sangyeol,Noh, Jungsik The Korean Data and Information Science Society 2013 한국데이터정보과학회지 Vol.24 No.6
This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.
Forecasting value-at-risk by encompassing CAViaR models via information criteria
Sangyeol Lee,Jungsik Noh 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.6
This paper proposes a new method of VaR forecasting using the conditional au-toregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.
Maximum entropy test for infinite order autoregressive models
Sangyeol Lee,Jiyeon Lee,Jungsik Noh 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.3
In this paper, we consider the maximum entropy test in infinite order autoregressive models. Its asymptotic distribution is derived under the null hypothesis. A bootstrap version of the test is discussed and its performance is evaluated through Monte Carlo simulations.
Test for Parameter Change in Linear Processes Based on Whittle's Estimator
Lee, Taewook,Lee, Sangyeol Taylor & Francis Inc. 2007 Communications in Statistics Vol.36 No.11
<P> In this article, we develop a cusum test for testing for parameter changes in linear processes based on Whittle's estimator. It is shown that under regularity conditions, the test statistic converges to the sup of a Brownian bridge. The result is particularly useful in handling the change point test in stationary ARMA processes. A simulation result is provided for illustration.</P>
Test for dispersion constancy in stochastic differential equation models
Lee, Sangyeol,Guo, Meihui John Wiley Sons, Ltd 2012 Applied stochastic models in business and industry Vol.28 No.4
<P>In this paper, we propose a constancy test for volatility in It ô processes based on discretely sampled data. The test statistic constitutes an integration of the Ljung–Box test statistic and the kurtosis statistic in the Jarque–Bera test. It is shown that under regularity conditions, the proposed test asymptotically follows a chi‐square distribution under the null hypothesis of constant volatility. To evaluate the test, empirical sizes and powers were examined through a simulation study. Analysis of real data including ultra‐high frequency transaction data and interest rates was also conducted for illustration. Copyright © 2011 John Wiley & Sons, Ltd.</P>
Maximum entropy test for infinite order autoregressive models
Lee, Sangyeol,Lee, Jiyeon,Noh, Jungsik The Korean Data and Information Science Society 2013 한국데이터정보과학회지 Vol.24 No.3
In this paper, we consider the maximum entropy test in in nite order autoregressiv models. Its asymptotic distribution is derived under the null hypothesis. A bootstrap version of the test is discussed and its performance is evaluated through Monte Carlo simulations.
PARAMETER CHANGE TEST FOR NONLINEAR TIME SERIES MODELS WITH GARCH TYPE ERRORS
Lee, Jiyeon,Lee, Sangyeol Korean Mathematical Society 2015 대한수학회지 Vol.52 No.3
In this paper, we consider the problem of testing for a parameter change in nonlinear time series models with GARCH type errors. We introduce two types of cumulative sum (CUSUM) tests: estimates-based and residual-based tests. It is shown that under regularity conditions, their limiting null distributions are the sup of independent Brownian bridges. A simulation study is conducted for illustration.