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Finite difference methods for estimating marginal risk contributions in asset management
Olschewsky, Michael; Lüdemann, Stefan; Poddig, Thorsten - In: Journal of risk 18 (2016) 5, pp. 63-99
Persistent link: https://www.econbiz.de/10011598391
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The impact of sovereign yield curve differentials on value-at-risk forecasts for foreign exchange rates
Fink, Holger Maria; Fuest, Andreas; Port, Henry - In: Risks : open access journal 6 (2018) 3, pp. 1-19
A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model … August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield difference as the …
Persistent link: https://www.econbiz.de/10011890808
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Forecasting VaR models under different volatility processes and distributions of return innovations
Dendramis, Yiannis; Spungin, Giles E.; Tzavalis, Elias - In: Journal of forecasting 33 (2014) 7, pp. 515-531
Persistent link: https://www.econbiz.de/10011282095
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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
of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector … 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 …
Persistent link: https://www.econbiz.de/10014497424
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Forecasting daily and monthly exchange rates with machine learning techniques
Plakandaras, Vasilios; Papadimitriou, Theophilos; … - In: Journal of forecasting 34 (2015) 7, pp. 560-573
Persistent link: https://www.econbiz.de/10011390457
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Inflation and unemployment forecasting with genetic support vector regression
Sermpinis, Georgios; Stasinakis, Charalampos; … - In: Journal of forecasting 33 (2014) 6, pp. 471-487
Persistent link: https://www.econbiz.de/10010426238
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Financial time series forecasting using empirical mode decomposition and support vector regression
Nava, Noemi; Di Matteo, Tiziana; Aste, Tomaso - In: Risks : open access journal 6 (2018) 1, pp. 1-21
We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes...
Persistent link: https://www.econbiz.de/10011811500
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The informational content of the term spread in forecasting the US inflation rate : a nonlinear approach
Plakandaras, Vasilios; Gkonkas, Periklēs; … - In: Journal of forecasting 36 (2017) 2, pp. 109-121
Persistent link: https://www.econbiz.de/10011729092
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Fama-French three versus five, which model is better? : a machine learning approach
Diallo, Boubacar; Bagudu, Aliyu; Zhang, Qi - In: Journal of forecasting 42 (2023) 6, pp. 1461-1475
Persistent link: https://www.econbiz.de/10014338932
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A hybrid model for forecasting realized volatility based on heterogeneous autoregressive model and support vector regression
Zhuo, Yue; Morimoto, Takayuki - In: Risks : open access journal 12 (2024) 1, pp. 1-16
In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the...
Persistent link: https://www.econbiz.de/10014480965