Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Anyfantaki, Sofia; Demos, Antonis

doi:10.1080/07474938.2014.966639

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…This issue is discussed in McAleer and Hafner (2014) and also in Chang and McAleer (2017). Typically the properties of EGARCH models have to be investigated empirically, as, for example, in Anyfantaki and Demos (2016). Some of the recent theoretical advances are made in Martinet and McAleer (2018), who show that the EGARCH(p, q) model can be derived from a stochastic process, and sufficient invertibility conditions can be stated in simple form.…”

Section: Empirical Illustrationmentioning

confidence: 99%

Higher Moment Constraints for Predictive Density Combination

2020

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The majority of financial data exhibit asymmetry and heavy tails, which makes forecasting the entire density critically important. Recently, a forecast combination methodology has been developed to combine predictive densities. We show that combining individual predictive densities that are skewed and/or heavy-tailed results in significantly reduced skewness and kurtosis. We propose a solution to overcome this problem by deriving optimal log score weights under Higher-order Moment Constraints (HMC). The statistical properties of these weights, such as consistency and asymptotic distribution, are investigated theoretically and through a simulation study. An empirical application that uses the S&P 500 daily index returns illustrates that the proposed HMC weight density combinations perform very well relative to other combination methods.

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Section: Empirical Illustrationmentioning

confidence: 99%

Higher Moment Constraints for Predictive Density Combination

2020

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“… See, for example, Karanasos & Kim , Chan & Gray and Anyfantaki & Demos . The EGARCH model is found to provide better predictions of volatility like the so‐called GJR asymmetric GARCH model of Glosten et al , and the asymmetric power ARCH model of Ding et al , .…”

mentioning

confidence: 99%

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

Dendramis

Spungin

Tzavalis

2014

Journal of Forecasting

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This paper provides clear-cut evidence that the out-of-sample VaR (value-at-risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime-switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime-switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered.

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“…Although time-varying GARCH-M models are commonly used in econometrics and finance, the recursive nature of conditional variance makes likelihood analysis computationally infeasible. Therefore, Anyfantaki and Demos (2016) suggested using Markov Chain Monte Carlo algorithm, which allowed classical estimators to be computed by simulating EM algorithm or only using simulated Bayes in O(T) operations (T is sample size), and derived the theoretical dynamic properties of time-varying parameter EGARCH (1,1)-M. Dias (2017) proposed an estimation strategy for stochastic time-varying risk premium parameters in time-varying GARCH-in-mean model, and Monte Carlo study showed that the algorithm had good finite sample properties.…”

Section: Time-varying Covariance Matrixmentioning

confidence: 99%

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Sun¹,

Liu

et al. 2019

JRFM

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The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR.

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Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Cited by 11 publications

References 22 publications

Higher Moment Constraints for Predictive Density Combination

Higher Moment Constraints for Predictive Density Combination

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

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