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Jump variation estimation with noisy high frequency financial data via wavelets

Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

Persistent link: https://www.econbiz.de/10011568279

Non-parametric estimation of copula parameters : testing for time-varying correlation

Gong, Jinguo; Wu, Weiou; McMillan, David G.; Shi, Daimin - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 19 (2015) 1, pp. 93-106

Persistent link: https://www.econbiz.de/10011311193

Modelling changes in the unconditional variance of long stock return series

Amado, Cristina; Teräsvirta, Timo - In: Journal of empirical finance 25 (2014), pp. 15-35

Persistent link: https://www.econbiz.de/10010462094

Variance reduction estimation for return models with jumps using gamma asymmetric kernels

Song, Yuping; Hou, Weijie; Zhou, Shengyi - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 23 (2019) 5, pp. 1-38

Persistent link: https://www.econbiz.de/10012198377

Do we need stochastic volatility and generalised autoregressive conditional heteroscedasticity? : Comparing squared end-of-day returns on ftse

Allen, David E.; McAleer, Michael - In: Risks : open access journal 8 (2020) 1/12, pp. 1-20

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken...

Persistent link: https://www.econbiz.de/10012203997