Forecasting realized volatility of oil futures market: A new insight

Wahab

et al. 2019

Self Cite

In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.

Section: Forecast and Evaluation Methodsmentioning

confidence: 99%

Section: Preliminary Datamentioning

confidence: 99%

Section: Cojumpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

Wahab

et al. 2019

Self Cite

“…Liu, Patton, and Sheppard (2015) find little evidence that 5-minute RV is outperformed by any other measures from 400 volatility estimators for 31 different financial assets spanning five asset classes. In addition, the 5minute RV is also widely used and recommended in a large number of studies (see, e.g., Andersen et al, 2007;Corsi et al, 2010;Ma, Li, et al, 2018;Ma, Wei, Liu, & Huang, 2018;Wang, Ma, Wei, & Wu, 2016). Along the same lines, we choose the 5-minute interval as our sampling frequency to measure RV.…”

Section: Data and Preliminary Analysismentioning

confidence: 99%

Out‐of‐sample volatility prediction: A new mixed‐frequency approach

Wang

et al. 2019

Self Cite

This paper proposes a new mixed‐frequency approach to predict stock return volatilities out‐of‐sample. Based on the strategy of momentum of predictability (MoP), our mixed‐frequency approach has a model switching mechanism that switches between generalized autoregressive conditional heteroskedasticity (GARCH)‐class models that only use low‐frequency data and heterogeneous autoregressive models of realized volatility (HAR‐RV)‐type that only use high‐frequency data. The MoP model simply selects a forecast with relatively good past performance between the GARCH‐class and HAR‐RV‐type forecasts. The model confidence set (MCS) test shows that our MoP strategy significantly outperforms the competing models, which is robust to various settings. The MoP test shows that a relatively good recent past forecasting performance of the GARCH‐class or HAR‐RV‐type model is significantly associated with a relatively good current performance, supporting the success of the MoP model.

Is implied volatility more informative for forecasting realized volatility: An international perspective

Liang

Wei

2020

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Inspired by the commonly held view that international stock market volatility is equivalent to cross-market information flow, we propose various ways of constructing two types of information flow, based on realized volatility (RV) and implied volatility (IV), in multiple international markets. We focus on the RVs derived from the intraday prices of eight international stock markets and use a heterogeneous autoregressive framework to forecast the future volatility of each market for 1 day to 22 days ahead. Our Diebold-Mariano tests provide strong evidence that information flow with IV enhances the accuracy of forecasting international RVs over all of the prediction horizons. The results of a model confidence set test show that a market's own IV and the first principal component of the international IVs exhibit the strongest predictive ability. In addition, the use of information flows with IV can further increase economic returns. Our results are supported by the findings of a wide range of robustness checks.