Forecasting realized volatility in a changing world: A dynamic model averaging approach

Wang, Yudong; Ma, Feng; Wei, Yu; Wu, Chongfeng

doi:10.1016/j.jbankfin.2015.12.010

Cited by 237 publications

(132 citation statements)

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“…Wang et al (2016) find that the in-sample predictive relationships are not constant but change over time. J, CJ and TJ indicate the jump component.…”

Section: In-sample Estimation Analysismentioning

confidence: 73%

“…To evaluate the economic value of volatility forecasts, we consider a mean-variance utility investor who allocates his or her assets between the stock index and the risk-free asset in accordance with the literature (see, e.g., Guidolin & Na, 2006;Neely et al, 2014;Rapach et al, 2010;Wang et al, 2016). Specifically, these investors are interested in how well these volatility forecasts do in asset allocation.…”

Section: Economic Value Evaluationmentioning

confidence: 99%

“…The seminal works of Andersen and Bollerslev (1998) and Andersen, Bollerslev, Diebold, and Ebens (2001) propose the realized volatility or realized variance (RV), which are defined as the sum of all available intraday high-frequency squared returns. Thus this model has garnered wide focus in academia (see, e.g., Andersen, Bollerslev, & Diebold, 2007;Bekaert & Hoerova, 2014;Bollerslev, Patton, & Quaedvlieg, 2015;Busch, Christensen, & Nielsen, 2011;Corsi, Pirino, & Reno, 2010;Duong & Swanson, 2015;Wang, Ma, Wei, & Wu, 2016;Wang, Pan, & Wu, 2017). Corsi (2009) propose a simple heterogeneous autoregressive model of realized volatility (HAR-RV) based on the heterogeneous market hypothesis, which can capture "stylized facts" in financial market volatility, such as long memory and multiscaling behavior, and have other advantages in forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…Parameter estimation results of the CG model over the out-of-sample period [Colour figure can be viewed at wileyonlinelibrary.com] 2005;Wang et al, 2016)…”

mentioning

confidence: 99%

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Forecasting realized volatility of oil futures market: A new insight

Wei

Liu

et al. 2018

Journal of Forecasting

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In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.

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“…Wang et al (2016) find that the in-sample predictive relationships are not constant but change over time. J, CJ and TJ indicate the jump component.…”

Section: In-sample Estimation Analysismentioning

confidence: 73%

Section: Economic Value Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Parameter estimation results of the CG model over the out-of-sample period [Colour figure can be viewed at wileyonlinelibrary.com] 2005;Wang et al, 2016)…”

mentioning

confidence: 99%

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Forecasting realized volatility of oil futures market: A new insight

Wei

Liu

et al. 2018

Journal of Forecasting

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“…For example, Silvennoinen and Thorp [73] have observed that higher stock market volatility increases the correlation of commodities prices with equity markets. Also, the volatility transmissions to and from stock markets and the oil market has been observed [74,75]. Similarly, as in the case of other factors, this relationship varies in time [76,77].…”

Section: Oil Price Driversmentioning

confidence: 75%

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Drachal

2018

Energies

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This article presents results from modelling spot oil prices by Dynamic Model Averaging (DMA). First, based on a literature review and availability of data, the following oil price drivers have been selected: stock prices indices, stock prices volatility index, exchange rates, global economic activity, interest rates, supply and demand indicators and inventories level. Next, they have been included as explanatory variables in various DMA models with different initial parameters. Monthly data between January 1986 and December 2015 has been analyzed. Several variations of DMA models have been constructed, because DMA requires the initial setting of certain parameters. Interestingly, DMA has occurred to be robust to setting different values to these parameters. It has also occurred that the quality of prediction is the highest for the model with the drivers solely connected with the stock markets behavior. Drivers connected with macroeconomic fundamental indicators have not been found so important. This observation can serve as an argument favoring the hypothesis of the increasing financialization of the oil market, at least in the short-term period. The predictions from other, slightly different modelling variations based on DMA methodology, have happened to be consistent with each other in general. Many constructed models have outperformed alternative forecasting methods. It has also been found that normalization of the initial data, although not necessary for DMA from the theoretical point of view, significantly improves the quality of prediction.

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Time‐Varying Parameter Realized Volatility Models

Wang

Pan

2016

Journal of Forecasting

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In this paper, we introduce the functional coefficient to heterogeneous autoregressive realized volatility (HAR‐RV) models to make the parameters change over time. A nonparametric statistic is developed to perform a specification test. The simulation results show that our test displays reliable size and good power. Using the proposed test, we find a significant time variation property of coefficients to the HAR‐RV models. Time‐varying parameter (TVP) models can significantly outperform their constant‐coefficient counterparts for longer forecasting horizons. The predictive ability of TVP models can be improved by accounting for VIX information. Copyright © 2016 John Wiley & Sons, Ltd.

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Forecasting realized volatility in a changing world: A dynamic model averaging approach

Cited by 237 publications

References 50 publications

Forecasting realized volatility of oil futures market: A new insight

Forecasting realized volatility of oil futures market: A new insight

Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework

Time‐Varying Parameter Realized Volatility Models

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