• Title/Summary/Keyword: conditional mean model

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Recent Review of Nonlinear Conditional Mean and Variance Modeling in Time Series

  • Hwang, S.Y.;Lee, J.A.
    • Journal of the Korean Data and Information Science Society
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    • 제15권4호
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    • pp.783-791
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    • 2004
  • In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

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비선형 평균 일반화 이분산 자기회귀모형의 추정 (Estimation of nonlinear GARCH-M model)

  • 심주용;이장택
    • Journal of the Korean Data and Information Science Society
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    • 제21권5호
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    • pp.831-839
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    • 2010
  • 최소제곱 서포트벡터기계는 비선형회귀분석과 분류에 널리 쓰이는 커널기법이다. 본 논문에서는 금융시계열자료의 평균 및 변동성을 추정하기 위하여 평균의 추정 방법으로는 가중최소제곱 서포트벡터기계, 변동성의 추정 방법으로는 최소제곱 서포트벡터기계를 사용하는 비선형 평균 일반화 이분산 자기회귀모형을 제안한다. 제안된 모형은 선형 일반화 이분산 자기회귀모형 및 선형 평균 일반화 이분산 자기회귀모형보다 더 나은 추정 능력을 가진다는 것을 실제자료의 추정을 통하여 보였다.

Lunar Effect on Stock Returns and Volatility: An Empirical Study of Islamic Countries

  • MOHAMED YOUSOP, Nur Liyana;WAN ZAKARIA, Wan Mohd Farid;AHMAD, Zuraidah;RAMDHAN, Nur'Asyiqin;MOHD HASAN ABDULLAH, Norhasniza;RUSGIANTO, Sulistya
    • The Journal of Asian Finance, Economics and Business
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    • 제8권5호
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    • pp.533-542
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    • 2021
  • The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

A Conditional Unrelated Question Model with Quantitative Attribute

  • Lee, Gi Sung;Hong, Ki Hak
    • Communications for Statistical Applications and Methods
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    • 제8권3호
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    • pp.753-765
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    • 2001
  • We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

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가스터빈 모사 연소기에서 선회 확산 화염의 연소특성 해석 (Simulation of Methane Swirl Flame in a Gas Turbine Model Combustor)

  • 정대로;허강열
    • 한국연소학회:학술대회논문집
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    • 한국연소학회 2007년도 제34회 KOSCO SYMPOSIUM 논문집
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    • pp.118-125
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    • 2007
  • The firtst-order conditional moment closure (CMC) model is applied to CH4/air swirl diffusion flame in a gas turbine model combustor. The flow and mixing fields are calculated by fast chemistry assumption with SLFM library and a beta function pdf for mixture fraction. RNG k-e model is used to consider the swirl flame in a confined wall. Reacting scalar fields are calculated by elliptic CMC formulation with chemical kinetic mechanism, GRI Mech 3.0. Validation is done against measurement data for mean flow and scalar fields in the model combustor [1]. Results show reasonable agreement with the mean mixture fraction and its variance, while temperature is overpredicted as the level of local extinction increases. The second-order CMC model is needed to consider local extinction with considerable conditional fluctuations near the nozzle.

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난류확산연소에서의 conditional moment closure modeling (Conditional moment closure modeling in turbulent nonpremixed combustion)

  • 허강열
    • 한국연소학회:학술대회논문집
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    • 한국연소학회 2000년도 제21회 KOSCO SYMPOSIUM 논문집
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    • pp.24-32
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    • 2000
  • A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OH in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

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난류확산연소에서의 Conditional Moment Closure Modeling (Conditional Moment Closure Modeling in Turbulent Nonpremixed Combustion)

  • 허강열
    • 한국연소학회지
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    • 제5권2호
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    • pp.9-17
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    • 2000
  • A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OR in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

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수정 아레니우스 모형에서 가족수명시험에 대한 조건부 신뢰구간 (Conditional Confidence Intervals for Accelerated Life Testing in Modified Arrhenius Model)

  • 박병구
    • 품질경영학회지
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    • 제25권3호
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    • pp.1-10
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    • 1997
  • In the context of accelerated life tests, procedures are given for estimating the parameters in the modified Arrhenius model and for estimating mean life at a given future stress level. The conditional confidence intervals are obtained by conditioning on ancillary statistics and pivotal quantity. Using the data of Tobias and Trindada(1986), we illustrate conditional confidence interval for parameters under use condition in the modified Arrhenius model.

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다변량회귀 조건부 평균모형에 대한 최적 차원축소 방법에서 차원수가 결과에 미치는 영향 (Effect of Dimension in Optimal Dimension Reduction Estimation for Conditional Mean Multivariate Regression)

  • 서은경;박종선
    • Communications for Statistical Applications and Methods
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    • 제19권1호
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    • pp.107-115
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    • 2012
  • 본 논문에서는 Yoo와 Cook (2007)에 의하여 제시된 다변량 회귀의 조건부 평균에 대한 최소 불일치 함수 접근법을 통한 최적 차원축소 부분공간의 추정에서 차원의 수가 추정된 선형결합들과 설명력 등에 어떤 영향을 미치는 지를 시뮬레이션 자료를 통하여 알아보았다. 그 결과 추정에 사용된 차원수에 따른 여러 결과들을 차원결정을 위한 검정과 함께 활용하면 모형에 필요한 차원수를 탐색하는데 매우 효과적임을 알 수 있었다.

Integer-Valued HAR(p) model with Poisson distribution for forecasting IPO volumes

  • SeongMin Yu;Eunju Hwang
    • Communications for Statistical Applications and Methods
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    • 제30권3호
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    • pp.273-289
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    • 2023
  • In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.