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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

Good-deal bounds for option prices under value-at-risk and expected shortfall constraints

Desmettre, Sascha; Laudagé, Christian; Sass, Jörn - In: Risks : open access journal 8 (2020) 4/114, pp. 1-22

In this paper, we deal with the pricing of European options in an incomplete market. We use the common risk measures Value-at-Risk and Expected Shortfall to define good-deals on a financial market with log-normally distributed rate of returns. We show that the pricing bounds obtained from the...

Persistent link: https://www.econbiz.de/10012390405

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

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

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

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

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

On GARCH and autoregressive stochastic volatility approaches for market calibration and option pricing

Pang, Tao; Zhao, Yang - In: Risks : open access journal 13 (2025) 2, pp. 1-24

heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In …-step-ahead volatility forecasts. The empirical results show that the ARSV(1) model outperforms the GARCH(1, 1) model in terms of the in … pricing call options, while the ARSV(1) model is significantly superior to the GARCH(1, 1) model regarding fitting and …

Persistent link: https://www.econbiz.de/10015334547

Historical perspectives in volatility forecasting methods with machine learning

Qiu, Zhiang; Kownatzki, Clemens; Scalzo, Fabien; Cha, … - In: Risks : open access journal 13 (2025) 5, pp. 1-24

of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to …

Persistent link: https://www.econbiz.de/10015408938

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