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Functional GARCH models : the quasi-likelihood approach and its applications

Cerovecki, Clément; Francq, Christian; Hörmann, Siegfried - In: Journal of econometrics 209 (2019) 2, pp. 353-375

Persistent link: https://www.econbiz.de/10012302614

On the correlations in linearized multivariate stochastic volatility models

Moussa, Karim - 2025

In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data …

Persistent link: https://www.econbiz.de/10015333113

An IID test for functional time series with applications to high-frequency VIX index data

Huang, Xin; Shang, Han Lin; Siu, Tak Kuen - In: Risks : open access journal 13 (2025) 2, pp. 1-25

To address a key issue in functional time series analysis on testing the randomness of an observed series, we propose an IID test for functional time series by generalizing the Brock-Dechert-Scheinkman (BDS) test, which is commonly used for testing nonlinear independence. Similarly to the BDS...

Persistent link: https://www.econbiz.de/10015333723

On GARCH and autoregressive stochastic volatility approaches for market calibration and option pricing

Pang, Tao; Zhao, Yang - In: Risks : open access journal 13 (2025) 2, pp. 1-24

heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In …-step-ahead volatility forecasts. The empirical results show that the ARSV(1) model outperforms the GARCH(1, 1) model in terms of the in … volatility, are not robust for out-of-sample option price predictions. …

Persistent link: https://www.econbiz.de/10015334547

Asymptotic expansion for some local volatility models arising in finance

Albeverio, Sergio; Cordoni, Francesco; Di Persio, Luca; … - In: Decisions in economics and finance : DEF ; a journal of … 42 (2019) 2, pp. 527-573

Persistent link: https://www.econbiz.de/10012127266

Long memory of financial time series and hidden Markov models with time‐varying parameters

Nystrup, Peter; Madsen, Henrik; Lindström, Erik - In: Journal of forecasting 36 (2017) 8, pp. 989-1002

Persistent link: https://www.econbiz.de/10011860941

Continuous time approximations to GARCH and stochastic volatility models

Lindner, Alexander M. - In: Handbook of financial time series, (pp. 481-496). 2009

Persistent link: https://www.econbiz.de/10003834175

Maximum likelihood estimation of continuous time stochastic volatility models with partially observed GARCH

Niu, Wei-fang - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 17 (2013) 4, pp. 421-438

Persistent link: https://www.econbiz.de/10009787977

l 1 - penalized likelihood smoothing of volatility processes allowing for abrupt changes

Neto, David; Sardy, Sylvain; Tseng, Paul - 2009

Persistent link: https://www.econbiz.de/10003926975

The dynamic Skellam model with applications

Koopman, Siem Jan; Lit, Rutger; Lucas, André - 2014

We introduce a dynamic statistical model for Skellam distributed random variables. The Skellam distribution can be obtained by taking differences between two Poisson distributed random variables. We treat cases where observations are measured over time and where possible serial correlation is...

Persistent link: https://www.econbiz.de/10010253460