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Assessing financial stability in turbulent times : a study of generalized autoregressive conditional heteroskedasticity-type Value-at-Risk model performance in Thailand's transport...

Danai Likitratcharoen; Lucksuda Suwannamalik - In: Risks : open access journal 12 (2024) 3, pp. 1-19

GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)-GARCH (1,1) VaR, GARCH (1,1)-M VaR, IGARCH (1,1) VaR, EWMA VaR … surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior … of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector …

Persistent link: https://www.econbiz.de/10014497424

Tail-dependence in stock-return pairs

Fortin, Ines; Kuzmics, Christoph - 2002

Persistent link: https://www.econbiz.de/10001736255

Risk analysis using regression quantiles : evidence from international equity markets

Guo, Hongtao; Lam, Miranda S.; Wu, Guojun; Xiao, Zhijie - In: The international journal of business and finance … 7 (2013) 2, pp. 1-15

Persistent link: https://www.econbiz.de/10009675606

Measurement of systemic risk in the Colombian banking sector

Rivera-Escobar, Orlando; Escobar, John Willmer; … - In: Risks : open access journal 10 (2022) 1, pp. 1-27

This paper uses three methodologies for measuring the existence of systemic risk in the Colombian banking system. The determination of its existence is based on implementing three systemic risk measures widely referenced in academic works after the subprime crisis, known as CoVaR, MES and SRISK....

Persistent link: https://www.econbiz.de/10012805886

How macroeconomic conditions affect systemic risk in the short and long-run?

Kurter, Zeynep O. - 2022

Persistent link: https://www.econbiz.de/10013272025

FRM: a financial risk meter based on penalizing tail events occurrence

Yu, Lining; Härdle, Wolfgang; Borke, Lukas; Benschop, Thijs - 2017

In this paper we propose a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter () of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly...

Persistent link: https://www.econbiz.de/10011598919

RiskAnalytics : an R package for real time processing of Nasdaq and Yahoo finance data and parallelized quantile lasso regression methods

Borke, Lukas - 2017

In order to integrate and facilitate the research, calculation and analysis methods around the Financial Risk Meter (FRM) project, the R package RiskAnalytics has been developed. Its main goal is to provide data processing and parallelized quantile lasso regression methods for risk analysis...

Persistent link: https://www.econbiz.de/10011619517

An AI approach to measuring financial risk

Yu, Lining; Härdle, Wolfgang; Borke, Lukas; Benschop, Thijs - In: The Singapore economic review 68 (2023) 5, pp. 1529-1549

Persistent link: https://www.econbiz.de/10014436192

Bidirectional linkage between inflation and inflation uncertainty – the case of Eastern European countries

Živkov, Dejan; Njegic, Jovan; Pecanac, Marko - In: Baltic Journal of Economics 14 (2014) 1-2, pp. 124-139

choosing an optimal Generalized Autoregressive Conditional Heteroskedasticity- (GARCH) type model. Subsequently, inflation and …

Persistent link: https://www.econbiz.de/10011868490

An empirical characterization of volatility dynamics in the DAX

Virla, Leonardo Quero - 2021

This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term …

Persistent link: https://www.econbiz.de/10012643577