關於金融資產波動度動態之兩議題

柯易辰

doi:10.6342/NTU202200191

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關於金融資產波動度動態之兩議題

Two Essays on Volatility Dynamics of Financial Assets

柯易辰 , Ph.D　　Advisor：洪茂蔚;王之彥　　

英文 DOI： 10.6342/NTU202200191

非仿射隨機波動度模型；線性漂移項；非線性漂移項；減幅方程式； HAR模型；已實現波動度；波動度結構； Nonaffine stochastic volatility model ； Linear drift ； Nonlinear drift ； Damping function ； HAR model ； Realized variance ； Volatility structure

Abstract │ Reference (48)

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

This thesis includes two financial empirical studies. The first is a comparative analysis of the damped constant elasticity variance (DCEV) model, the log variance (LV) model, and other state-of-the-art stochastic volatility models. The second applies the model of Elton et al. (1990) to identify the key realized variance (RV) in the heterogeneous autoregressive (HAR) model.
    In the first study, we comprehensively compare the empirical performance of the DCEV model proposed by Hung et al. (2021) with that of the LV, CEV, and nonlinear drift (NLD) models used widely in the literature. The empirical results indicate that the DCEV model still exhibits fitting performance superior to that of competing models, especially for crisis periods in the market.
    Corsi (2009) proposes the HAR model, a simple regression based on three explanatory variables, including one-day, one-week (5-day), and one-month (22-day) RVs. Many studies verify that the forecasting ability of the HAR model with an indexed RV lag of (1, 5, 22) is superior to that of complicated stochastic volatility models. In the second study, we propose applying the model of Elton et al. (1990) to identify the key RVs by systematically extracting information from the past 35-day RV structure instead of using lag (1, 5, 22). The empirical results reveal that given a sufficient sample period, the HAR model with key RVs as the explanatory variables efficiently improves on the forecasting ability of the original HAR model.

Reference ( 48 ) 〈TOP〉

Ahn, D. and B. Gao. 1999. A Parametric Nonlinear Model of Term Structure Dynamics. Review of Financial Studies 12: 721–762.
Ait-Sahalia, Y. 1996. Testing Continuous-Time Models of the Spot Interest Rate. Review of Financial Studies 9: 385–426.
Ait-Sahalia, Y. 2002. Maximum-Likelihood Estimation of Discretely Sampled Diffusions: A Closed-Form Approach. Econometrica 70: 223–262.
Ait-Sahalia, Y. and R. Kimmel. 2007. Maximum Likelihood Estimation of Stochastic Volatility Models. Journal of Financial Economics 83: 413–452.
Andersen, T. G., T. Bollerslev, and F. X.Diebold. 2007. Roughing it up: Including Jump Components in the Measurement, Modeling, and Forecasting Return Volatility. Review of Economics and Statistics 89: 701–720.

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