Threshold Approaches to Asymmetric Models for Heteroscedastic Time Series
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Asymmetric volatility or the so called leverage effect has recently attracted considerable attention owing to empirical evidences from various time series. This review paper discusses two threshold approaches to account for asymmetries in the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) context. The first one involves directly modeling the volatility via threshold functions such as in threshold GARCH models (see, Hwang and Kim [1]). The second approach is indirect for which errors are asymmetric, for instance, skewed t-distributed. A threshold concept is introduced for errors (Hwang [2]) as an indirect asymmetry in volatility. Quasi-likelihood and Godambe optimum scores are discussed to estimate parameters. As an illustration for cell lineage studies, a random coefficient model in bifurcating process is discussed.
Abstract
Asymmetric volatility or the so called leverage effect has recently attracted considerable attention owing to empirical evidences from various time series. This review paper discusses two threshold approaches to account for asymmetries in the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) context. The first one involves directly modeling the volatility via threshold functions such as in threshold GARCH models (see, Hwang and Kim [1]). The second approach is indirect for which errors are asymmetric, for instance, skewed t-distributed. A threshold concept is introduced for errors (Hwang [2]) as an indirect asymmetry in volatility. Quasi-likelihood and Godambe optimum scores are discussed to estimate parameters. As an illustration for cell lineage studies, a random coefficient model in bifurcating process is discussed.
참고문헌 (18)
- Hwang SY, "Power transformation and threshold modeling for ARCH innovations with applications to tests for ARCH structure", Stoch Proc Appl, 110, pp.295~314 (2004)
- S.Y. Hwang, "Feasible optimum Godambe scores for a semi-parametric GARCH time series", Journal of the Korean Statistical Society, 46(1), pp.104~112 (2017), Elsevier BV[DOI]
- Hwang SY, "Stationarity and moment structure for Box-Cox transformed threshold GARCH(1,1) processes", Stat Probabil Lett, 68, pp.209~220 (2004)
- Andersen TG, "Handbook of financial time series" (2009), Springer
- Choi MS, "Asymmetric GARCH processes featuring both threshold effect and bilinear structure", Stat Probabil Lett, 82, pp.419~426 (2012)
- Fernandez C, "On Bayesian modeling of fat tails and skewness", J Am Stat Assoc, 93, pp.359~371 (1998)
- S. Y. Hwang, "Quasilikelihood and quasi-maximum likelihood for GARCH-type processes: Estimating function approach", Journal of the Korean Statistical Society, 43(4), pp.631~641 (2014), 한국통계학회
- Li CW, "On a double-threshold autoregressive heteroscedastic time series model", J Appl Econom, 11, pp.253~274 (1996)
- Tsay RS., "Analysis of financial time series" (2010), Wiley
- Azzalini A., "A class of distributions which includes the normal ones", Scand J Stat, 12, pp.171~178 (1985)
- Gupta AK, "Characterization of the skew normal distribution", Ann I Stat Math, 56, pp.351~360 (2004)
- Hwang SY, "Contemporary developments in statistical theory", pp.9~28 (2014), Springer
- Godambe VP., "The foundation of finite sample estimation in stochastic processes", Biometrika, 72, pp.419~428 (1985)
- Cowan R, "The bifurcating autoregression model in cell lineage studies", Biometrics, 42, pp.769~783 (1986)
- 황선영, "Contemporary review on the bifurcating autoregressive models : Overview and perspectives", 한국데이터정보과학회지, 25(5), pp.1137~1149 (2014), 한국데이터정보과학회[DOI]
- Hwang SY., "An overview on models for tree-indexed time series", QBS, 30, pp.9~11 (2011)
- Hwang SY, "Modeling and large sample estimation for multi-casting autoregression", Stat Probabil Lett, 79, pp.1943~1950 (2009)
- Nicholls DF, "Random coefficient autoregressive models: an introduction", Lecture notes in statistics, 11 (1982), Springer