Future Volatility Research Articles

Simmering tensions on the Russia–Ukraine border and natural gas futures prices: identifying the impact using new hybrid GARCH

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Exploiting the sentiments: A simple approach for improving cross hedging effectiveness

This paper introduces a novel hedging strategy based on textual information for cross-hedging. The strategy allows the optimal hedge ratio to vary with the sentiment extracted from online oil news, leading to an improvement in cross-hedging effectiveness. We find that the sentiment hedging strategy outperforms traditional time-varying and static hedging strategies in both in-sample and out-of-sample scenarios, with statistical and economic significance. This superior performance benefits from the well-documented predictive power of sentiment on futures volatility. Furthermore, we confirm an asymmetric effect of sentiment on hedging effectiveness, with negative sentiment having a greater impact on hedge ratios. Finally, extreme sentiment can trade off the negative relationship between volatile hedge ratios and hedging performance, potentially leading to superior hedging performance compared to normal sentiment, which is robust when considering transaction costs. Our findings highlight the importance of sentiment for estimating the optimal hedge ratio and should be considered for risk management.

Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model

This study examines the predictive ability of nine high-frequency jumps on the Chinese crude oil futures volatility using a series of the Heterogeneous Autoregressive (HAR) models. Out-of-sample empirical results indicate that among the nine high-frequency jump tests, the JO jump component is powerful because the prediction model including this component demonstrates superior predictive performance. Compared to other competing models, the model incorporating JO jump component, jump intensity, and Markov-regime achieves higher predictive accuracy. During the outbreak of the COVID-19 pandemic and periods of high volatility, this new model continues to exhibit strong predictive capability for volatility in the Chinese oil futures market. This study provides novel insights into forecasting volatility in the Chinese oil market under the presence of extreme shocks.

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

AbstractThis paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.

More is better? The impact of predictor choice on the INE oil futures volatility forecasting

This paper aims to address the predictor choice issue in forecasting volatility of INE oil futures by a comprehensive comparative study with a large number of predictive variables and applying machine learning models along with their interpretability tools. The main finding is that the selection of predictors is crucial for improving volatility forecasting accuracy, but it is not always the case that including more predictive variables leads to better forecasting results, even for machine learning models. Specifically, this paper has five major findings: (1) A few variables can significantly improve forecasting accuracy independently, but their contribution is limited. (2) Increasing the number of predictors from specific categories (market sentiment indicators, crude oil futures prices from other exchanges, and energy market indicators) helps to enhance forecasting accuracy. (3) Low-frequency variables have a weak effect on improving the daily volatility. (4) Ensemble tree models perform better than traditional machine learning models based on variable selection with dynamic parameter optimization, even without much parameter tuning. The above findings still hold true under a series of robustness tests and economic value assessments. These findings provide substantial evidence for addressing the issues of model and variable choice in crude oil futures volatility forecasting.

Intraday volatility patterns from short-dated options

We propose a nonparametric estimator for the deterministic periodic component of volatility from short-dated options within an in-fill asymptotic setting. The estimator uses options with zero and one day to expiration sampled at high-frequency during a trading day. At each point in time, we aggregate the options to form nonparametric estimates of conditional risk-neutral expectations of future integrated return variation for the two available option tenors. A suitable ratio of these estimates removes the stochastic components of the conditional expectations of future volatility, up to asymptotically higher-order terms, and allows to form estimates of the deterministic periodic component of volatility. We derive a Central Limit Theorem for the estimator, with its rate of convergence determined from the mesh of the strike grid and the length of the time to expiration of the options. The newly-developed estimation procedure is applied to S&P 500 index options data.

Modeling Historical Volatility of 10-year Chinese Treasury Bond Futures: A Comparative Analysis of MLE and MCMC Approaches

Modeling Historical Volatility of 10-year Chinese Treasury Bond Futures: A Comparative Analysis of MLE and MCMC Approaches

Extrapolative Uncertainty and Household Economic Behavior

We examine the uncertainty in households’ expectations regarding macroeconomic outcomes, namely inflation and the rate of nationwide home price growth. We document that people extrapolate from the instability of their personal and local environment when assessing the future volatility of these macroeconomic variables. Consequently, there are within-person spillovers in subjective uncertainty regarding different economic outcomes. This extrapolative behavior is more pronounced among lower-numeracy individuals, and it persists throughout the business cycle. People with more uncertain macroeconomic expectations are more likely to engage in precautionary behaviors regarding consumption and equity market exposure and are more concerned about credit market access. This paper was accepted by David Simchi-Levi, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00820 .

Can the global financial cycle and economic conditions predict real estate market volatility?

ABSTRACT This study examines the predictive power of the newly proposed global financial cycle (GFC) and global economic conditions (GECON) indicators for real estate market volatility. To this end, we extend the GARCH-MIDAS model by simultaneously incorporating realized volatility and macro indicators along with the asymmetry effect. The empirical results reveal that both GFC and GECON provide significant predictive information on real estate market volatility. The predictive performance of GFC is superior to that of GECON, implying the important role of finance in the real estate market. The relationship between GFC/GECON and the future volatility of the real estate market is positive, and the short- and long-run asymmetry effects are evidenced.

Volatility and jump with intraday periodicity and truncated power variation in Chinese yuan-US dollar exchange rates

ABSTRACT This study analyzes the discrete jump volatility of the Chinese yuan/U.S. dollar exchange rate returns using high-frequency five-minute returns from June 2012 to April 2021. Using periodicity filters of volatility, we conduct volatility jump tests for finite and infinite exchange rate jumps, and the truncated power variation does not exhibit upward or downward jumps. We use combined intraday jump statistics with local robust variance. All empirical results show that if we do not include periodicity filters of volatility, such as MAD and ShortH, the five-minute returns of the Chinese yuan/US dollar exchange rates have much lower intraday jump probabilities. Thus, the volatility and jumps of the Chinese yuan/US dollar ratio will be underestimated if we do not consider the periodicity filters of the volatility in the 2010s. We need to include periodicity filters of volatility and jumps to reduce forecast errors in future Chinese Yuan/US dollar exchange rates so that we obtain a better measure of current volatility and generate better hedging strategies for future volatility.

Futures trading costs and market microstructure invariance: Identifying bet activity

AbstractMarket microstructure invariance (MMI) stipulates that trading costs of financial assets are driven by the volume and volatility of bets, but these variables are inherently difficult to identify. With futures transactions data, we estimate bet volume as the trading volume of brokerage firms that trade on behalf of their clients and bet volatility as the trade‐related component of futures volatility. We find that the futures bid–ask spread lines up with bet volume and bet volatility as predicted by MMI, and that intermediation by high‐frequency traders does not interfere with the MMI relation.

Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees

This paper investigates the performance of ensemble boosting trees in forecasting volatility of China's crude oil futures by combining rich feature variables and multiple volatility forecasting models. The empirical results demonstrate that ensemble boosting tree models significantly outperform the HAR-RV model and traditional machine learning models, with the CatBoost and the LightGBM having the best forecasting performance, and that these conclusions hold up under robustness tests. Using the SHAP values model interpretability instrument, this paper analyzes the model interpretability of LightGBM and CatBoost in terms of the drivers of volatility forecasting, the contribution of variables in a specific period, and the performance of variables in forecasting outliers. It is discovered that macroeconomic variables and HAR-type variables have different forecasting contributions in CatBoost and LightGBM, and that the contribution of different variables to the forecasting window varies significantly within a single interval. In addition, the paper concludes that there is heterogeneity in the forecast contribution of the same predictor across models, so the selection of variables for forecasting volatility should be based on the actual situation. Lastly, additional analysis confirms that the ensemble boosting tree models also have a high economic value.

Impact of night trading sessions on volatility of USD futures market in Thailand

Prior to September 2021, USD Futures could only be traded during the regular hours in Thailand Futures Exchange (TFEX). An additional trading session at night, while trading in both London and New York exchanges is active, enables investors to better handle their foreign exchange exposure or speculative needs of the moment. However, understanding volatility behavior is crucial for achieving successful trading. Therefore, this study investigates the impact of night trading sessions on the USD Futures volatility using GARCH family models. The USD Futures market volatility is examined through comparative analysis before and after the introduction of the night trading session. Both TARCH and EGARCH models have revealed no existence of leverage effect over the sample period - from January 2, 2020 to December 30, 2022. The GARCH model has proved to be the most accurate model for describing USD Futures volatility. Following the launch of nighttime trading, USD Futures market has experienced higher and more persistent volatility. In response to an increase in the volatility of USD Futures, TFEX should increase its margin requirement and monitor the speculative movements in futures market for their possible destabilizing effect. Investors should also adjust their hedge ratio to manage risk more appropriately and incorporate an extended period of increased uncertainty into their trading strategies.

Time–frequency correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets

PurposeThis paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.Design/methodology/approachWith daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.FindingsThe empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.Originality/valueThe MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

Forecasting realized volatility of crude oil futures prices based on machine learning

AbstractExtending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning‐generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

Assessing the Risk of Bitcoin Futures Market: New Evidence

AbstractThe main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.

Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model

The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange's Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model

Technological innovation and its influence on energy risk management: Unpacking China’s energy consumption structure optimisation amidst climate change

In the context of intensifying climate challenges, adept energy risk management is more pertinent than ever. This research pioneers an in-depth exploration into China’s pronounced reliance on high-polluting fossil fuels, utilising a decade’s worth of provincial data (2010–2020) to shed light on the intricate dynamics between technological innovation and energy consumption structure refinement. Notably, our findings unveil that technological advancements act as catalysts in streamlining energy consumption structures, serving as a bulwark against emergent climate-related risks. Yet, this positive trajectory is not immune to disruptions: volatility in crude oil futures prices has the potential to dampen these benefits, ushering in heightened financial risks. Our work further underscores pronounced regional variances; technological innovation yields diminished returns in the central and western regions compared to their eastern counterparts. An intriguing observation is the resilience exhibited by coal-dependent provinces to technological evolution, pointing towards entrenched energy infrastructure challenges. Crucially, this study is among the first to identify the dual roles of industrial structure evolution and energy pricing dynamics as mediators in energy risk management. Drawing from these insights, we advocate for a proactive harnessing of technological innovation, not merely as a tool, but as an imperative to drive China’s energy transformation, foster sustainable consumption, and lay the foundation for a fortified green and low-carbon technological ecosystem.

Forecasting EUA futures volatility with geopolitical risk: evidence from GARCH-MIDAS models

This paper examines whether the information contained in geopolitical risk (GPR) can improve the forecasting power of price volatility for carbon futures traded in the EU Emission Trading System. We employ the GARCH-MIDAS model and its extended forms to estimate and forecast the price volatility of carbon futures using the most informative GPR indicators. The models are examined for both statistical and economic significance. According to the results of the Model Confidence Set tests for the full-sample and sub-sample data, we find that the extended model, which accounts for the threat of geopolitical risk, exhibits superior forecasting ability for the full-sample data, while the model that includes drastic changes in geopolitical risk in Phase II and the model that considers serious geopolitical risk in Phase III have the best predictive power. Moreover, all GPR-related variables we use contribute to increasing economic gains. In particular, the threat of geopolitical risk contains valuable information for future EUA futures volatility and can provide the highest economic gains. Therefore, carbon market investors and policymakers should pay great attention to geopolitical risk, especially its threat, in risk and portfolio management.