지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Discrete- and continuous-time stochastic volatility (SV) models are introduced to explain conditional heterogeneity and dependence along with conditional leptokurtosis found in higher order moments of two stock market index return processes. We use the efficient method of moments (EMM) procedure combined with the seminonparametric (SNP) model as the score generator to estimate SV models. The EMM is applicable to a variety of asset pricing models where the moment restrictions contain unobservable state vector and improves efficiency of the estimator without resorting to the likelihood approach. By employing EMM in estimating two SV models with SNP auxiliary models, we aim to evaluate the performance of the SNP conditional density function and the SV models in characterizing non-Gaussianity of the conditional volatility process. As seen from the empirical results, the SV models fail to fit the various scores considered in the EMM estimation. The SV models are not appropriate for capturing the characteristics of non-Gaussianity, fat-tailed behavior and conditional heterogeneity of the observed data. We also find that the SNP models are more appropriate in modeling non-Gaussianity and non-linear dynamics along with conditional heterogeneity of the conditional distribution in the index return process.
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UCI(KEPA) : I410-151-24-02-089573459