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Forecasting financial returns volatility : a GARCH-SVR model

Sun, Hao; Yu, Bo - In: Computational economics 55 (2020) 2, pp. 451-471

Persistent link: https://www.econbiz.de/10012223641

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

GARCHNet: Value‑at‑risk forecasting with GARCH models based on neural networks

Buczynski, Mateusz; Chlebus, Marcin - In: Computational economics 63 (2024) 5, pp. 1949-1979

Persistent link: https://www.econbiz.de/10014550845

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

An application of the IFM method for the risk assessment of financial instruments

Pons, Adrià; Cristobal-Fransi, Eduard; Vintrò, Carla; … - In: Computational economics 61 (2023) 1, pp. 295-315

Persistent link: https://www.econbiz.de/10014228427

Volatility forecasting using support vector regression and a hybrid genetic algorithm

Santamaría-Bonfil, Guillermo; Frausto-Solís, Juan; … - In: Computational economics 45 (2015) 1, pp. 111-133

Persistent link: https://www.econbiz.de/10010511334

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

Forecasting US unemployment with radial basis neural networks, Kalman filters and support vector regressions

Stasinakis, Charalampos; Sermpinis, Georgios; … - In: Computational economics 47 (2016) 4, pp. 569-587

Persistent link: https://www.econbiz.de/10011712477

Economic growth prediction using optimized support vector machines

Emsia, Elmira; Coskuner, Cagay - In: Computational economics 48 (2016) 3, pp. 453-462

Persistent link: https://www.econbiz.de/10011712511

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

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