Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect

Wang, Jiqian; Ma, Feng; Wahab, M.I.M.; Huang, Dengshi

doi:10.1002/for.2752

Cited by 17 publications

(2 citation statements)

References 49 publications

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“… The LHAR‐CJ model The third model, proposed by Corsi and Reno (2012), is the LHAR‐CJ model. In addition to return jumps, this model also captures the leverage effect where a negative return shock leads to larger volatility increase than a positive shock of the same size (see Choi & W, 2018; Wang et al, 2021, for instance). The LHAR‐CJ model is specified as follows:

\begin{matrix} right σ_{t : t + h - 1} = & left ω + β_{d} {\hat{T C}}_{t - 1 : t - 1} + β_{w} {\hat{T C}}_{t - 5 : t - 1} + β_{m} {\hat{T C}}_{t - 22 : t - 1} \\ right & left + θ_{d} \log ({\hat{T J}}_{t - 1} + 1) \\ right & left + θ_{w} \log ({\hat{T J}}_{t - 5 : t - 1} + 1) + θ_{m} \log ({\hat{T J}}_{t - 22 : t - 1} + 1) \\ right & left + ω_{d} r_{t - 1}^{-} + ω_{w} r_{t - 5 : t - 1}^{-} + ω_{m} r_{t - 22 : t - 1}^{-} + ε_{t}, \end{matrix}

where

r_{t_{1} : t_{2}} = \frac{1}{false(t_{2} - t_{1} + 1 false)} falsefalse <...

…”

Section: Econometric Frameworkmentioning

confidence: 99%

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Fei

Liu

Wen

2023

Journal of Forecasting

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In this paper, we study the predictive performance of two kinds of volatility estimators: the realized volatility (RV) type and duration‐based ones. This is motivated by the theoretical and empirical support for these distinct estimators. We use intraday data for 218 component stocks of the CSI 300 index in the Chinese equity market from 2010–2019 and perform in‐ and out‐of‐sample 1‐, 5‐, and 22‐day ahead volatility forecasts from combinations of volatility models and these estimators. We show that, although empirically more efficient with the US data, the duration‐based estimators fail to compete statistically, or in terms of economic value, with RV‐type ones in the Chinese market. We perform a comprehensive set of simulations to rationalize these results and show that duration‐based estimators underperform as they cannot handle the occasional heightened level of volatility in the Chinese market.

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\begin{matrix} right σ_{t : t + h - 1} = & left ω + β_{d} {\hat{T C}}_{t - 1 : t - 1} + β_{w} {\hat{T C}}_{t - 5 : t - 1} + β_{m} {\hat{T C}}_{t - 22 : t - 1} \\ right & left + θ_{d} \log ({\hat{T J}}_{t - 1} + 1) \\ right & left + θ_{w} \log ({\hat{T J}}_{t - 5 : t - 1} + 1) + θ_{m} \log ({\hat{T J}}_{t - 22 : t - 1} + 1) \\ right & left + ω_{d} r_{t - 1}^{-} + ω_{w} r_{t - 5 : t - 1}^{-} + ω_{m} r_{t - 22 : t - 1}^{-} + ε_{t}, \end{matrix}

where

r_{t_{1} : t_{2}} = \frac{1}{false(t_{2} - t_{1} + 1 false)} falsefalse <...

…”

Section: Econometric Frameworkmentioning

confidence: 99%

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Fei

Liu

Wen

2023

Journal of Forecasting

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“…First, our paper is closely linked to recent literature on the detection of China's crude oil volatility predictability. Wang et al (2021) investigate the prediction ability of jump, jump intensity, and leverage effect for China's crude oil futures emplying different kinds of HAR-type models. However, we use a benchmark model that is better than HAR.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

Chen

2022

Resources Policy

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Modelling the Chinese crude oil futures returns through a skew‐geometric Brownian motion correlated with the market volatility index process for pricing financial options

Bufalo,

Fanelli

2024

Appl Stoch Models Bus & Ind

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In this paper we model the dynamics of the Chinese crude oil futures returns by using a skew‐geometric Brownian motion correlated with the market volatility, which is taken as a square‐root stochastic process. We use the OVX index data as proxy for market volatility. We validate the proposed model in terms of accuracy of its calibrations through an in‐sample simulation. Instead, out‐of‐sample simulations are used to show that a correlated skew‐geometric Brownian motion is more appropriate for modelling the Chinese returns compared to a single skew‐geometric Brownian motion in terms of forecasts. Furthermore, we price an American call option on the Chinese futures by using a recursively scheme based on a closed‐form formula, and an alternative Monte Carlo approach, for the related European call option. We show that our call price estimates are very close to market values and our model generally outperforms many benchmarks in literature, such as the Barone‐Adesi and Whaley formula and its generalizations.

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Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect

Cited by 17 publications

References 49 publications

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic

Modelling the Chinese crude oil futures returns through a skew‐geometric Brownian motion correlated with the market volatility index process for pricing financial options

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