Search Results - subject:"Error Distributions"

Risk model validation: An intraday VaR and ES approach using the multiplicative component GARCH

Summinga-Sonagadu, Ravi; Narsoo, Jason - In: Risks 7 (2019) 1, pp. 1-23

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five...

Persistent link: https://www.econbiz.de/10013200428

Risk model validation : an intraday VaR and ES approach using the multiplicative component GARCH

Summinga-Sonagadu, Ravi; Narsoo, Jason - In: Risks : open access journal 7 (2019) 1/10, pp. 1-23

Persistent link: https://www.econbiz.de/10012018629

Nigeria stock market volatility in comparison with some countries: Application of asymmetric GARCH models

Uyaebo, Stephen O.; Atoi, Victor N.; Usman, Farida - In: CBN Journal of Applied Statistics 06 (2015) 2, pp. 133-160

This study estimated Asymmetric generalized autoregressive conditional heteroscadasticity models with endogenous break dummy on two innovation assumptions using daily all share index of Nigeria, Kenya, United States, Germany, South Africa and China spanning from February 14, 2000 to February 14,...

Persistent link: https://www.econbiz.de/10011482621

Nigeria stock market volatility in comparison with some countries : application of asymmetric GARCH models

Uyaebo, Stephen O.; Atoi, Victor N.; Usman, Farida - In: CBN journal of applied statistics 6 (2015) 2, pp. 133-160

Persistent link: https://www.econbiz.de/10011460578

Testing volatility in Nigeria stock market using GARCH models

Atoi, Ngozi V. - In: CBN Journal of Applied Statistics 05 (2014) 2, pp. 65-93

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … asymmetric volatility models each in Normal, Student's-t and generalized error distributions with the view to selecting the best … recommends that empirical works should consider alternative error distributions with a view to achieving a robust volatility …

Persistent link: https://www.econbiz.de/10011518789

Testing volatility in Nigeria stock market using GARCH models

Atoi, Ngozi V. - In: CBN journal of applied statistics 5 (2014) 2, pp. 65-93

Persistent link: https://www.econbiz.de/10011489480

Ensuring the Validity of the Micro Foundation in DSGE Models

Andreasen, Martin Møller - School of Economics and Management, University of Aarhus - 2008

The presence of i) stochastic trends, ii) deterministic trends, and/or iii) stochastic volatility in DSGE models may imply that the agents' objective functions attain infinite values. We say that such models do not have a valid micro foundation. The paper derives sufficient conditions which...

Persistent link: https://www.econbiz.de/10005440061

Small-sample properties of estimators in an ARCH(1) and GARCH(1,1) model with a generalized error distribution: A robustness study

Pauly, Ralf; Kosater, Peter - 2005

mixture of two symmetric generalized error distributions. This data generating process allow to reproduce the stylized facts …

Persistent link: https://www.econbiz.de/10010289317

Small-sample Properties of Estimators in an ARCH(1) and GARCH(1,1) Model with a Generalized Error Distribution: a Robustness Study

Pauly, Ralf; Kosater, Peter - Institut für Empirische Wirtschaftsforschung, … - 2005

mixture of two symmetric generalized error distributions. This data generating process allow to reproduce the stylized facts …

Persistent link: https://www.econbiz.de/10008739196

Optimal Forecast Combination Under General Loss Functions and Forecast Error Distributions

Elliott, Graham; Timmermann, Allan - Department of Economics, University of California-San … - 2002

Existing results on the properties and performance of forecast combinations have been derived in the context of mean squared error loss. Under this loss function empirical studies have generally found that estimates of optimal forecast combination weights lead to higher losses than...

Persistent link: https://www.econbiz.de/10010536497