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Related papers: Modeling Risk via Realized HYGARCH Model

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This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance. The study integrates high-throughput finite element simulations and Gaussian Process…

Machine Learning · Computer Science 2024-08-08 Shadab Anwar Shaikh , Harish Cherukuri , Kranthi Balusu , Ram Devanathan , Ayoub Soulami

Several phenomena are available representing market activity: volumes, number of trades, durations between trades or quotes, volatility - however measured - all share the feature to be represented as positive valued time series. When…

Statistical Finance · Quantitative Finance 2021-07-14 Fabrizio Cipollini , Giampiero M. Gallo

In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to…

Machine Learning · Statistics 2017-05-03 Syed Ali Asad Rizvi , Stephen J. Roberts , Michael A. Osborne , Favour Nyikosa

Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We…

Computation · Statistics 2023-08-30 Hanwen Xuan , Luca Maestrini , Feng Chen , Clara Grazian

Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among matrix entries, conducting support…

Methodology · Statistics 2017-09-01 Xi Chen , Weidong Liu

This paper presents a comparative analysis of univariate and multivariate GARCH-family models and machine learning algorithms in modeling and forecasting the volatility of major energy commodities: crude oil, gasoline, heating oil, and…

Econometrics · Economics 2024-05-31 Seulki Chung

In this article, by using composite asymmetric least squares (CALS) and empirical likelihood, we propose a two-step procedure to estimate the conditional value at risk (VaR) and conditional expected shortfall (ES) for the GARCH series.…

Statistics Theory · Mathematics 2018-07-05 Sheng Wu , Yi Zhang , Jun Zhao , Liming Shen

This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semi-parametric regression model based on…

Risk Management · Quantitative Finance 2024-02-13 Cathy W. S. Chen , Takaaki Koike , Wei-Hsuan Shau

We propose a continuous-time Markov-switching generalized autoregressive conditional heteroskedasticity (COMS-GARCH) process for handling irregularly spaced time series (TS) with multiple volatilities states. We employ a Gibbs sampler in…

Methodology · Statistics 2020-12-15 Yinan Li , Fang Liu

We propose a general class of INteger-valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing time-varying mean and dispersion parameters, which we call time-varying dispersion INGARCH (tv-DINGARCH)…

One of the most important features of financial time series data is volatility. There are often structural changes in volatility over time, and an accurate estimation of the volatility of financial time series requires careful…

Methodology · Statistics 2022-10-24 Huaiyu Hu , Ashis Gangopadhyay

This paper provides a probabilistic and statistical comparison of the log-GARCH and EGARCH models, which both rely on multiplicative volatility dynamics without positivity constraints. We compare the main probabilistic properties (strict…

Statistics Theory · Mathematics 2013-04-11 Christian Francq , Olivier Wintenberger , Jean-Michel Zakoïan

This paper compares the Value--at--Risk (VaR) forecasts delivered by alternative model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The direct VaR estimate provided by the…

Computation · Statistics 2015-02-17 Mauro Bernardi , Leopoldo Catania

For a given time horizon DT, this article explores the relationship between the realized volatility (the volatility that will occur between t and t+DT), the implied volatility (corresponding to at-the-money option with expiry at t+DT), and…

Pricing of Securities · Quantitative Finance 2009-01-16 Gilles Zumbach

We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low-, respectively, high-frequency features in the data. We derive the…

Statistical Finance · Quantitative Finance 2020-06-08 Alessandra Amendola , Vincenzo Candila , Fabrizio Cipollini , Giampiero M. Gallo

Although quantile regression to calculate risk measures has been widely established in the financial literature, when considering data observed at mixed--frequency, an extension is needed. In this paper, a model is suggested built on a…

Statistical Finance · Quantitative Finance 2023-03-17 Vincenzo Candila , Giampiero M. Gallo , Lea Petrella

This paper proposes a hybrid methodology to improve the approximation of SABR (Stochastic Alpha Beta Rho) implied volatility by combining analytical structure with machine learning. The approach augments the neural-network input…

Computational Finance · Quantitative Finance 2026-05-08 Adil Reghai , Lama Tarsissi , Gérard Biau , Alex Lipton

Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-Risk (VaR). VaR estimates based on linear and parametric…

Statistical Finance · Quantitative Finance 2020-09-16 Marius Lux , Wolfgang Karl Härdle , Stefan Lessmann

In an era when derivatives is getting popular, risk management has gradually become the core content of modern finance. In order to study how to accurately estimate the volatility of the S&P 500 index, after introducing the theoretical…

Mathematical Finance · Quantitative Finance 2021-07-21 Wen Su

This paper presents a novel approach to stochastic volatility (SV) modeling by utilizing nonparametric techniques that enhance our ability to capture the volatility of financial time series data, with a particular emphasis on the…

Computation · Statistics 2025-02-18 Yudong Feng , Ashis Gangopadhyay