Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

Borup, Daniel; Jakobsen, Johan Stax

doi:10.1080/14697688.2019.1614653

Cited by 37 publications

(25 citation statements)

References 81 publications

(98 reference statements)

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“…When predicting financial market volatility, macroeconomic indicators are important (Andersen et al [4]; Conrad and Loch [10]; Dorion [11]). e GARCH-MIDAS model has been the most popular model adopted to investigate the correlations between aggregate financial volatility and macroeconomic or financial variables (Conrad et al [12]; Conrad et al [13]; Pan et al [14]; Su et al [15]; Conrad and Kleen [6]; Opschoor et al [16]; Dominicy and Vander Elst [17]; Lindblad [18]; Amendola et al [19]; Conrad et al [12]; and Borup and Jakobsen [20]).…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

Yang

Liu

et al. 2022

Complexity

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This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

Yang

Liu

et al. 2022

Complexity

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“…By allowing for a mixed‐frequency setting, this approach bridges the gap between daily stock returns and low‐frequency (e.g., monthly, quarterly) explanatory variables. For further applications of GARCH‐MIDAS‐type models see, for example, Conrad, Loch, and Rittler (), Opschoor, van Dijk, and van der Wel (), Dominicy and Vander Elst (), Lindblad (), Amendola, Candila, and Scognamillo (), Pan, Wang, Wu, and Yin (), Conrad, Custovic, and Ghysels (), and Borup and Jakobsen (). For a recent survey on multiplicative component models see Amado, Silvennoinen, and Teräsvirta ().…”

Section: Introductionmentioning

confidence: 99%

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

Conrad

Kleen

2020

J of Applied Econometrics

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Summary We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn (Review of Economics and Statistics, 2013, 95, 776–797). In those models volatility is decomposed into a short‐term GARCH component and a long‐term component that is driven by an explanatory variable. We derive the kurtosis of returns, the autocorrelation function of squared returns, and the R2 of a Mincer–Zarnowitz regression and evaluate the QMLE and forecast performance of these models in a Monte Carlo simulation. For S&P 500 data, we compare the forecast performance of GARCH‐MIDAS models with a wide range of competitor models such as HAR (heterogeneous autoregression), realized GARCH, HEAVY (high‐frequency‐based volatility) and Markov‐switching GARCH. Our results show that the GARCH‐MIDAS based on housing starts as an explanatory variable significantly outperforms all competitor models at forecast horizons of 2 and 3 months ahead.

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“…Under the usual regularity conditions, standard errors for the elements ofθ T can be easily obtained from the numerically approximated observed Fisher information matrix and inference can be performed relying on the asymptotic normality ofθ T . In order to double check the validity of the standard asymptotic results on the distribution ofθ T , as in Borup and Jakobsen (2019), exploiting the dynamically complete nature of the proposed model, we have implemented a parametric Bootstrap resampling algorithm along the lines described in Paparoditis and Politis (2009). The main steps of the Bootstrap resampling procedure are summarized below.…”

Section: Estimation and Inferencementioning

confidence: 99%

Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics

Gerlach

Naimoli

Storti

2020

Quantitative Finance

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This paper proposes novel approaches to the modeling of attenuation bias effects in volatility forecasting. Our strategy relies on suitable generalizations of the Realized GARCH model by Hansen et al. (2012) where the impact of lagged realized measures on the current conditional variance is weighted according to the accuracy of the measure itself at that specific time point. This feature allows assigning more weight to lagged volatilities when they are more accurately measured. The ability of the proposed models to generate accurate forecasts of volatility and related tail risk measures, Value-at-Risk and Expected Shortfall, is assessed by means of an application to a set of major stock market indices. The results of the empirical analysis show that the proposed specifications are able to outperform standard Realized GARCH models in terms of out-of-sample forecast performance under both statistical and economic criteria.

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Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model

Cited by 37 publications

References 81 publications

[Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

[Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics

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