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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

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

Volatility modeling and dependence structure of ESG and conventional investments

Górka, Joanna; Kuziak, Katarzyna - In: Risks : open access journal 10 (2022) 1, pp. 1-25

, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail …

Persistent link: https://www.econbiz.de/10012805838

Volatility timing in CPF investment funds in Singapore : do they outperform non-CPF funds?

Shen, Xiaoyi; Tsui, Albert K.; Zhang, Zhaoyong - In: Risks : open access journal 7 (2019) 4/106, pp. 1-16

will provide the CPF board with a new method for risk classification. We employ the GARCH models and modified factor models …

Persistent link: https://www.econbiz.de/10012127925

Modeling high frequency data with long memory and structural change : A-HYEGARCH model

Shi, Yanlin; Yang, Yang - In: Risks : open access journal 6 (2018) 2, pp. 1-28

In this paper, we propose an Adaptive Hyperbolic EGARCH (A-HYEGARCH) model to estimate the long memory of high frequency time series with potential structural breaks. Based on the original HYGARCH model, we use the logarithm transformation to ensure the positivity of conditional variance. The...

Persistent link: https://www.econbiz.de/10011811728

Empirical testing of models of autoregressive conditional heteroscedasticity used for prediction of the volatility of Bulgarian investment funds

Petrova, Mariana; Todorov, Teodor - In: Risks : open access journal 11 (2023) 11, pp. 1-30

investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1 … concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH … models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty …

Persistent link: https://www.econbiz.de/10014436423

Forecasting bitcoin volatility using hybrid GARCH models with machine learning

Zahid, Mamoona; Iqbal, Farhat; Koutmos, Dimitrios - In: Risks : open access journal 10 (2022) 12, pp. 1-18

-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities …

Persistent link: https://www.econbiz.de/10014230957

When the US stock market becomes extreme?

Aboura, Sofiane - In: Risks : open access journal 2 (2014) 2, pp. 211-225

Over the last three decades, the world economy has been facing stock market crashes, currency crisis, the dot-com and real estate bubble burst, credit crunch and banking panics. As a response, extreme value theory (EVT) provides a set of ready-made approaches to risk management analysis....

Persistent link: https://www.econbiz.de/10010399734

Tail risk and extreme events : connections between oil and clean energy

Di Febo, Elisa; Foglia, Matteo; Angelini, Eliana - In: Risks : open access journal 9 (2021) 2/39, pp. 1-13

Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect...

Persistent link: https://www.econbiz.de/10012483185

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

assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based … that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the …’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH …

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