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Jointly modeling autoregressive conditional mean and variance of non-negative valued time series

Kawakatsu, Hiroyuki - In: Econometrics : open access journal 7 (2019) 4/48, pp. 1-19

This paper considers observation driven models with conditional mean and variance dynamics for non-negative valued time series. The motivation is to relax the restriction imposed on the higher order moment dynamics in standard multiplicative error models driven only by the conditional mean...

Persistent link: https://www.econbiz.de/10012160740

The stock returns volatility based on the GARCH (1,1) model : the superiority of the truncated standard normal distribution in forecasting volatility

Gulay, Emrah; Emec, Hamdi - In: Iranian economic review : journal of University of Tehran 23 (2019) 1, pp. 87-108

Persistent link: https://www.econbiz.de/10012152550

Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures

Gong, Xiao-Li; Liu, Xi-Hua; Xiong, Xiong - In: Pacific-Basin finance journal 55 (2019), pp. 95-109

Persistent link: https://www.econbiz.de/10012169513

Time-varying volatility and the power law distribution of stock returns

Warusawitharana, Missaka - In: Journal of empirical finance 49 (2018), pp. 123-141

Persistent link: https://www.econbiz.de/10012117726

Modelling and forecasting inflation in Egypt : univariate and multivariate approaches

Ahmed, Doaa Akl; Abdelsalam, Mamdouh Abdelmoula Mohamed - In: Middle East development journal : a publication of the … 9 (2017) 1, pp. 127-159

Persistent link: https://www.econbiz.de/10011722559

Empirical performance of GARCH models with heavy-tailed innovations

Guo, Zi-Yi - 2017 - This version: August 2017

We introduce a new type of heavy-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten-Jagannathan-Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy-tailed distribution, the Student's t...

Persistent link: https://www.econbiz.de/10011723904

Value at risk (VaR) analysis for fat tails and long memory in returns

Günay, Samet - In: Eurasian economic review : a journal in applied … 7 (2017) 2, pp. 215-230

Persistent link: https://www.econbiz.de/10011684346

Modeling dependency of volatility on sampling frequency via delay equations

Luong, Chuong; Dokučaev, Nikolaj G. - In: Annals of financial economics 11 (2016) 2, pp. 1-12

Persistent link: https://www.econbiz.de/10011685676

Value-at-Risk under Lévy GARCH models : evidence from global stock markets

Slim, Skander; Koubaa, Yosra; BenSaïda, Ahmed - In: Journal of international financial markets, … 46 (2017), pp. 30-53

Persistent link: https://www.econbiz.de/10011745291

Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models : a comparison based on normal and Student's t-error distribution

Abdullah, S. M.; Siddiqua, Salina; Siddiquee, Muhammad … - In: Financial innovation : FIN 3 (2017) 18, pp. 1-19

Background: Modeling exchange rate volatility has remained crucially important because of its diverse implications …. This study aimed to address the issue of error distribution assumption in modeling and forecasting exchange rate volatility …-of-sample volatility forecasting, AR(2)–GARCH(1, 1) is considered the best. …

Persistent link: https://www.econbiz.de/10011747702