Future Volatility Research Articles

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures.

Volatility Contagion from Bulk Shipping and Petrochemical Industries to Oil Futures Market during the Economic Uncertainty

The purposes of the research have evidenced the spillover effects of oil-related factors in the oil market and the leading indexes of petrochemical commodities and the bulk shipping markets. The research gap was fitted and explored the effects associated with leading indexes for the shipping and petrochemical markets on the oil market during the US-China trade war, which is seldom bridged with significant relations in the history of oil. The scope of data for the period from 4 January 2016, through 31 August 2022, were analyzed using a generalized autoregressive conditional heteroskedastic mixed data sampling model as methodology of mix frequency to examine volatility spillover of four research hypotheses from the bulk shipping and petrochemical markets to the oil market. Main contributions revealed that spillover from the bulk shipping and petrochemical commodity markets transmitted significant volatility to West Texas Intermediate (WTI) oil returns after the US-China trade war began, a trend that has continued throughout the COVID-19 era until Ukraine–Russia war. These rare events indicate that the realized volatility derived from these market variables can be used to track the more significant contagions on WTI futures volatility in this empirical research than the weak relation in past studies.

The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models

This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.

Jumps in the Chinese crude oil futures volatility forecasting: New evidence

This study examines whether incorporating jumps and jump size can enhance the predictive ability of Chinese crude oil futures market realized volatility within the MIDAS model framework, incorporating Markov-switching. Our out-of-sample analysis indicates that the Markov-switching mixed data sampling model incorporating large and small jumps (MS-MIDAS-LMLS) performs better than other models in forecasting the volatility of Chinese crude oil futures. To ensure the robustness of our findings, we conducted various sensitivity test. The results consistently exhibit strong predictive performance of the MS-MIDAS-LMLS model. Additionally, we explored the impact of jump size on forecasting performance using an alternative jump test method (the ABD test) and different volatility regimes, with similar results indicating the positive impact of incorporating jump size on forecasting accuracy. Overall, our results underscore the potential benefit of integrating jumps and jump size within the MIDAS model framework to enhance the accuracy of volatility forecasting of Chinese crude oil futures market volatility.

Multiscale Price Discovery in the Global Futures Markets: Evidence from Wavelet Analysis

The sharp increase in liquidity has exacerbated volatility in futures markets. The shocks in volatility patterns have triggered the urgent need to re-examine the efficiency of futures markets, but this time on a time scale. In this study, we examine the effectiveness of global futures markets as a reference for future prices. We perform spectrogram analysis to determine the signal sensitivity of both markets, as expressed by the association between the spot and futures markets. We also observe the correlation pattern of spot and futures co-movements in the time-frequency domain. Our study shows that agricultural and energy markets are inefficient in the short term. The low short-term positive correlation leads to a temporary divergence in spot and futures prices, which provides a profit opportunity for futures contract speculators.

INE oil futures volatility prediction: Exchange rates or international oil futures volatility?

This study examines the predictive performances of volatility information from international oil futures volatility and exchange rates for Shanghai International Energy Exchange (INE) oil futures volatility. Various empirical findings show that the predictive performance of international oil futures volatility is superior to that of the exchange rate. In addition, we examine whether regime switching is efficient, and the results show that time-varying regime switching plays a satisfactory role. Moreover, these results are robust even during some special periods, including the COVID-19 pandemic, the Russia–Ukraine conflict, and different business cycles. This study provides new insights into the volatility prediction of the INE oil futures market.

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.

The Impact of COVID-19 on the Volatility of Copper Futures

The COVID-19 pandemic has introduced significant uncertainty across various economic sectors, most notably in the industrial sector due to the high volatility in copper futures markets. These markets play a crucial role in the construction, electrical networks, electronic products, and industrial machinery industries. Therefore, the aim of this study is to evaluate the impact of the COVID-19 pandemic on the volatility of copper futures returns from 2 January 2018 to 30 December 2022 using an extended ARMA(m,n)–GARCH(p,q) framework. According to the results, the occurrence of the pandemic nearly doubled the long-run equilibrium volatility of returns compared to the values obtained in the pre-pandemic period. Hence, we conclude that the COVID-19 pandemic has a significant influence on the volatility in the copper futures market.

Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach

This study proposes a new VMD-ICEEMDAN-LSTM model, which combines secondary decomposition with long short-term memory neural networks (LSTM) to forecast the realized volatility (RV) of Chinese crude oil futures. The RV sequence is first decomposed into subcomponents and residuals through variational mode decomposition (VMD). Then, the iterative complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is applied to perform a secondary decomposition on the residuals. Finally, we apply LSTM to forecast all decomposed components, and then combine all forecasting results to obtain our final forecast value. Our results show that the VMD-ICEEMDAN-LSTM model significantly outperforms existing individual and combination models.

Beware of Extreme Investor Sentiments! Indian Evidence on the Performance of Neuro-specific Options Volatility Trading Strategies on the Facets of COVID-19

This study investigates the dynamic relationship between noise trader sentiment and excessive volatility in the Indian financial market during the COVID-19 outbreak. It proposes novel options trading strategies to ensure profitability in times of irrational exuberance and to satisfy diverse investment requirements of volatility traders, which arise from the varying levels of stimulating neurotransmitters, namely dopamine and serotonin in the human body. Empirical results show that the incremental information content of sentiment measures is vital in forecasting future volatility and the proposed options trading strategies effectively accomplish the neuro-specific intentions of the traders during extreme volatility in the Indian equity market. JEL Codes: G12, G13

Price discovery and triangular arbitrage in currency markets

Price discovery is the process by which markets incorporate the relevant information to arrive at the efficient price of an asset. We study the price discovery process in JPY/EUR cross-rates and the rates derived from exchanges of JPY/USD and USD/EUR indirectly. The results highlight the role of the USD as a vehicle currency in enhancing price efficiency through the triangular arbitrage. During financial crisis periods, the implied JPY/EUR rates have even more contribution to price discovery than direct rates. We show the dominant price discovery of implied JPY/EUR rates relates to the lower transaction cost. Upon the release of macroeconomic announcements in Japan and Europe zone, the trading cost advantage enhances price discovery of implied rates even more. The more contribution to price discovery of implied rates predicts the higher future volatility of direct JPY/EUR rates, indicating the slower response of direct rates to the information shock and the subsequent adjustment resulting in higher variations in the direct rates.

Chinese agricultural futures volatility: New insights from potential domestic and global predictors

This paper investigates whether the potential predictors from China and globally can efficiently predict Chinese agricultural futures volatility by adopting the REGARCH-MIDAS framework. We highlight the predictability of numerous Chinese potential predictors for forecasting ten agricultural futures volatility, which is relatively better than that of global potential predictors. Robustness tests such as different realized measure and different forecasting window confirm the above conclusions. Performances of predictors during different volatility levels, before and during the COVID-19 pandemic are further discussed. This paper tries to shed new light on the volatility prediction of Chinese agricultural futures markets.

Macroeconomic attention and oil futures volatility prediction

This paper mainly checks whether the macroeconomic attention indices have valuable information to predict the oil futures volatility. Results show that macroeconomic attention indices are able to predict the oil futures volatility. In addition, based on several dimensionality reduction methods, we find that the scaled principal component analysis (SPCA) model has better predictive performances than other dimensionality reduction methods. Especially, the least absolute shrinkage and selection operator method (LASSO) has the best predictive performance. During the COVID-19 period, LASSO model with the macroeconomic attention indices can still have superior performances. This paper tries to show new evidence based on macroeconomic attention indices for oil market volatility.

Multi-factor Analysis of Option Pricing (BSM) and Prediction of Pricing Direction Under the Convergence of Phenomenal Worlds

In the development involving options pricing, models with data-based predictions of future volatility at their core have dominated, with their inclusion of elements such as implied volatility and the implication of Brownian motion, normal distributions under a variety of programmatic analyses acting to generate range values, structuring models that are evidence of the gradual formation of pricing concepts as trading researchers uncover predictions of asset values, and surfacing impediments to moving their pricing ranges to specificity. The development of the BSM is widely regulated by the mutual proximity between the acceptance of the model concept by traders in this period and the degree of sophistication in dealing with limited rationality. Facilitating efficient control of options markets and outlining later pricing directions. The initial understanding of the various interpretations of finite and infinite rationality of information, influenced by the complexity of specific value manipulation. The extension of uncertainty in its role generates the form of the scientific matrix of opposing terms - related phenomenal world concepts, regulated by the role of complex financial markets (the scientific matrix brought by the economists at the beginning of the market development contains its pricing model development), embodied by the transfer of the phenomenal world. The alternative phenomenal world model formed by the extension will be justified by considering the integration of feedback with external financial markets.

Global energy supply risk: Evidence from the reactions of European natural gas futures to Nord Stream announcements

Motivated by recent geopolitical challenges to globalization, finance scholarship has a growing interest in the risks to economies of key supply chains. We explore the effect of Nord Stream pipeline development on daily natural gas prices in Europe. Reviewing a comprehensive list of 24 announcements related to Nord Stream pipeline developments, stoppages, and sanctions for 2013–2022, we investigate their direct impact on the volatility and returns of Dutch TTF natural gas futures. Employing bootstrap quantile regression, we find that the effects of such announcements are highly significant, especially during periods of low TTF volatility and returns distributions. Moreover, results show that both COVID-19 and the Russia-Ukraine war strenghtened the correlation between Nord Stream pipeline announcements and TTF market reactions, consistent with energy markets pricing geopolitical risks. Results, reflective of informed trading, warn of concentration risk in European gas markets, suggesting the need for policy actions to reduce supply-chain dependence. Taken together, we support the importance of diversifying both natural gas supply infrastructures and providers to mitigate geopolitical risk exposures and associated welfare costs to the real economy.

Exploring the relationship between global economic policy and volatility of crude futures: A two-factor GARCH-MIDAS analysis

This study investigates the impact of global economic policy on the volatility of crude oil futures using a two-factor GARCH-MIDAS (Mixed Data Sampling) analysis. The core of the problem statement is the need to understand how global economic policy affects the volatility of crude oil futures, which has significant implications for investors, decision-makers, and market participants. The study aims to determine the correlation between critical global economic policy variables and crude oil futures volatility. This study combines the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework and the Mixed Data Sampling (MIDAS) method to create a Two-Factor GARCH-MIDAS model. This model thoroughly analyzes the long- and short-term dynamics between economic policy and crude oil futures volatility by incorporating macroeconomic and financial market data. The analysis covers a specific period from 2010 to 2021 and considers both prosperous and challenging economic times. The study's narrow geographic focus on a region with significant global economies and oil markets ensures a thorough representation of monetary and fiscal policies and changes in crude oil futures prices. The analysis provided insight into the complex link between volatility in crude oil futures and global economic policy. The results provide insightful information about how changes in interest rates, fiscal policies, and trade regulations affect the volatility of crude oil futures. The study advances knowledge of the connections between economic policy and energy markets, enabling investors and decision-makers in the international oil market to make more informed choices.

Good volatility, bad volatility, and the cross section of cryptocurrency returns

This paper examines the predictability of realized volatility measures (RVM), especially the realized signed jumps (RSJ), on future volatility and returns. We confirm the existence of volatility persistence and future volatility is more strongly related to the volatility of past positive returns than to that of negative returns in the cryptocurrency market. RSJ-sorted cryptocurrency portfolios yield statistically and economically significant differences in the subsequent portfolio returns. After controlling for cryptocurrency market characteristics and existing risk factors, the differences remain significant. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.

The Chinese oil futures volatility: Evidence from high-low estimator information

This paper mainly investigates whether the high-low estimator has valuable information to predict the Chinese oil futures volatility. The results show that the high-low estimator constructed based on daily prices contains useful information to predict the Chinese oil futures volatility. Moreover, adding regime switching to the models is helpful to improve forecasting accuracy, especially combining regime switching and the high-low estimator. This paper tries to provide new evidence for Chinese oil futures volatility prediction.

Oil futures volatility prediction: Bagging or combination?

This paper compares the predictive performance of the bagging method and traditional combination models for forecasting oil futures volatility, using economic policy uncertainty (EPU) indices and macroeconomic variables as predictors. Our empirical findings indicate that the bagging method outperforms the conventional combination models, demonstrating the effectiveness of machine learning combination models. These results are confirmed by different evaluation methods, alternative forecasting methods, and alternative oil futures, and hold up during the COVID-19 pandemic and various business cycles. Furthermore, we show that EPU indices are more useful than macroeconomic variables for forecasting oil volatility during the COVID-19 pandemic. Thus, our analysis provides new insights into combination forecasts.

The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests

This paper uses nonparametric causality-in-quantiles tests to examine the asymmetric effects of climate risk perception (CRP) on the thermal and coking coal futures high-frequency returns and volatilities. The results show that CRP significantly impacts the dynamic high-frequency returns of the coal futures market, with volatility indicators exhibiting asymmetry at different percentiles and being more pronounced in a downward market. The influence of CRP on dynamic coal futures mainly transmits through continuous components, while its impact on coking coal futures primarily transmits through jump parts. Additionally, the positive and negative volatilities of coal futures are asymmetrically affected by CRP. By incorporating the climate risk perception factor, investors can better predict price fluctuations in the coal market. This study provides an important supplement to the theory of pricing climate risks, and it is beneficial for formulating financial policies related to climate risk management and promoting the sustainable development of the coal industry.