Market Volatility Prediction Research Articles

Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach

This study comprehensively investigates stock market volatility based on over one hundred monthly macroeconomic variables, applying machine learning models. Methodological contribution integrating the random forest (RF) with the least absolute shrinkage and selection operator methods (LASSO). Importantly, the RF-LASSO model can robustly achieve the best forecasting performance under different circumstances. In addition, we focus on model explanation from different perspectives based on permutation importance and shapley additive explanation (SHAP) methods. This study illuminates novel insights into the realm of stock market volatility, harnessing the transformative potential of machine learning methodologies.

A Study of Stock Market Volatility Prediction Based on Traditional Regression and Intelligent Algorithms

China's stock market has been turbulent since the reform and opening up, often with the risk of plummeting, and in recent years it has been even more volatile, while the national level's concern for stock market stability is increasing day by day, and research on the stability of China's stock market is imminent. In this paper, on the basis of previous research and traditional macro fundamental analysis, firstly, we use LASSO regression analysis to initially determine the influence indicators that are causally related to stock market stability, and then test the significance of the model coefficients, and amend the original model by using multivariate linear regression, to further screen out the significant influencing factors; secondly, we use support vector machine regression (SVM) and random forest regression to fit the stock market volatility in the machine learning method. regression to fit the stock market volatility, determine the importance ratio of different characteristic variables in the model, and analyse the factors affecting the prediction of stock market volatility; finally, the LASSO regression is combined with machine learning to establish an improved model, and the screened indicator factors are fitted with machine learning models to further deepen the prediction of stock market volatility. Stock market oscillations and volatility are further deepened to measure the stability of the Chinese stock market.

Implementation of deep learning models in predicting ESG index volatility

The consideration of environmental, social, and governance (ESG) aspects has become an integral part of investment decisions for individual and institutional investors. Most recently, corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts. While stock market prediction is a complex and challenging task, several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices. To address this challenge, we propose an integrated computational framework to implement deep learning model architectures, specifically long short-term memory (LSTM), gated recurrent unit, and convolutional neural network, to predict the volatility of the ESG index in an identical environment. A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data, technical indicators, and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction. The performance of the constructed models was evaluated using standard assessment metrics. Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model. Furthermore, a series of statistical analyses was conducted to validate the robustness and reliability of the model. Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.

Exploring the impact of cluster bagging-based weather attention on oil market volatility under the GARCH-MIDAS framework

ABSTRACT Many studies have confirmed the impact of weather and Google Trends attention on oil markets. However, research examining the predictive influence of weather attention on oil price volatility is lacking. To fill this research gap, we have developed an extended GARCH-MIDAS forecasting model based on TBATS and clustering bagging. We utilize weather-related keywords from Google Trends to obtain weather attention data and assess its predictive capability. By decomposing the data using TBATS and combining the decomposed trend and seasonal components with the remaining portion through clustering bagging and arithmetic mean aggregation, we construct a series of GARCH-MIDAS models, and specifically focus on the clustering bagging-based TBATS decomposition GARCH-MIDAS model. Empirical analysis demonstrates a significant impact of weather attention magnitude on in-sample testing, with the clustering bagging-based TBATS model outperforming other models in out-of-sample forecasting. Our findings provide new evidence that incorporating weather attention information into a GARCH-MIDAS model can improve prediction results and highlight the influence of weather attention on future oil price fluctuations. Finally, robustness analysis confirms the robustness of the proposed model. The proposed extended GARCH-MIDAS model based on TBATS and clustering bagging offers new insights into oil market volatility prediction.

International stock market volatility: A global tail risk sight

This paper constructs a global tail risk (GTR) index and investigates the role of GTR in predicting the volatility of international stock markets. The results emphasize that GTR contains valuable information to predict the stock volatility of group (7) (G7) countries. In addition, accounting for the information of GTR and regime switching together can further improve the forecasting accuracy of international stock market volatility, especially considering the time-varying regime switching. The results are robust in different robustness checks and even during the global financial crisis period. Our paper tries to provide new evidence for tail risk in international stock market volatility prediction.

Forecasting crude oil market volatility: A comprehensive look at uncertainty variables

Uncertainty variables involving diverse aspects play leading roles in determining oil price movements. This study aims to improve the aggregate crude oil market volatility prediction based on a large set of uncertainty variables from a comprehensive viewpoint. Specifically, we apply three shrinkage methods, namely, forecast combination, dimension reduction, and variable selection, to extract valuable predictive information in a data-rich world. The empirical results show that the forecasting power of the individual uncertainty index is not satisfactory. By contrast, all shrinkage models, particularly the supervised machine learning techniques, demonstrate outstanding predictability of oil market volatility, which tends to be strong during business recessions. Notably, the sizeable economic gains confirm the superior forecasting performance of our comprehensive framework. We provide solid evidence that the two option-implied volatility variables uniformly serve as the best two predictors.

Cross-market information transmission and stock market volatility prediction

The deep integration of global economic and financial activities accelerates the information transmission across financial markets. The cross-market information has become a crucial factor to influence the stock market fluctuation. This paper investigates the explanatory power and impacting mechanism of cross-market information flow in the prediction of Chinese stock market volatility. The empirical results show that the cross-market information flow exhibits significant linear and nonlinear influences on Chinese stock market volatility, and it also appears term-heterogeneous on the prediction accuracy, i.e., the cross-market information flow significantly contributes to medium-term and long-term prediction of Chinese stock market volatility, and the improvement of prediction accuracy is mainly due to nonlinear mechanism, whereas the cross-market information flow performs less value for short-term volatility prediction.

Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

Stock market volatility prediction: Evidence from a new bagging model

The purpose of this study is to investigate which model can improve the precision of the categorical economic policy uncertainty indices in predicting volatility in the U.S. stock market. In this study, a new model is constructed by combining autoregressive model and bagging method. The empirical outcomes indicate that machine learning models outperform traditional forecasting models and that the new model constructed in this study has the best forecasting ability. We perform robustness tests using an alternative stock index, alternative forecasting windows, and different economic cycles. The results show that these findings are robust. We hope to provide new insights into the application of the bagging method in stock market volatility forecasting.

The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching

Applying the multivariate heterogeneous autoregressive (MHAR-RV-DCC) model, this article considers the structural mutation characteristics of oil futures and the U.S. stock markets’ information fluctuations, and embeds Markov regime switching (MS) to investigate the prediction performance of the crude oil market volatility prediction model under the transition of high and low states, and explores whether the Markov mechanism conversion multivariate model constructed by nonparametric measurement variables will help the government, scholars and practitioners to judge the future market. We test the in-sample fitting and out-of-sample forecasting. In comparison, our newly proposed multivariate MHAR-RV-DCC model and MS-MHAR-RV-DCC model have stronger predictive capabilities than other high-frequency volatility prediction models, the MS-MHAR-RV-SJV-DCC has the best predictive ability. The above empirical results not only affirm the advantages of the newly constructed multivariate and Markov regime switching models in the application of crude oil futures market forecasting but also broaden the research ideas and specific methods for the characterization and prediction of crude oil market volatility in the future.

A sentiment analysis approach to the prediction of market volatility.

Prediction and quantification of future volatility and returns play an important role in financial modeling, both in portfolio optimisation and risk management. Natural language processing today allows one to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. We found that there is evidence of correlation between sentiment and stock market movements. Moreover, the sentiment captured from news headlines could be used as a signal to predict market returns; we also found that the same does not apply for volatility. However, for the sentiment found in Twitter comments we obtained, in a surprising finding, a correlation coefficient of -0.7 (p < 0.05), which indicates a strong negative correlation between negative sentiment captured from the tweets on a given day and the volatility observed the next day. It is important to keep in mind that stock volatility rises greatly when the market collapses but not symmetrically so when it goes up (the so-called leverage effect). We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modeling, based on Latent Dirichlet Allocation, in order to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modeling even on modest (essentially personal) architecture our classifier achieved a directional prediction accuracy for volatility of 63%.

Air quality index and the Chinese stock market volatility: Evidence from both market and sector indices

This paper mainly examines whether the air quality index can forecast the Chinese stock market volatility. By comparing the predictability of air quality index, leverage components, jump components, and overnight information, we find that air quality index is more efficient to predict the stock market volatility, during medium-term and long-term horizons. Importantly, from both market and sector indices, we find the air quality index is still more informative than the leverage components, jump components, and overnight information for forecasting the stock market volatility. In addition, leverage components have better performances during a longer horizon for both market and sector indices. This paper tries to provide new evidence for stock market volatility prediction based on the Chinese aggregate stock market and different stock sectors.

Modeling and managing stock market volatility using MRS-MIDAS model

This paper adds the decomposed components of realized volatility to investigate China's stock market volatility based on the mixed data sampling (MIDAS) framework. Empirical results show that considering extreme negative volatility and extreme positive volatility, and moderate volatility can have a significantly better performance than the benchmark model for predicting the Chinese stock market volatility. Importantly, we find that taking the regime switching into consideration can further improve forecasting accuracy. The results are robust in alternative evaluation method, different forecasting windows, the direction-of-change test, and China's stock bubble period, showing decomposing the realized volatility into extreme negative volatility and extreme positive volatility, and moderate volatility can further improve the forecasting accuracy of the models. This paper tries to give new evidence to stock market volatility prediction.

Oil futures volatility predictability: New evidence based on machine learning models

This paper comprehensively examines the connection between oil futures volatility and the financial market based on a model-rich environment, which contains traditional predicting models, machine learning models, and combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, particularly the ensemble models and neural network models. Most interestingly, we consider the “forecast combination puzzle” in machine learning models, and find that combination models continue to have more satisfactory performances in all types of situations. We also discuss the model interpretability and each indicator's contribution to the prediction. Our paper provides new insights for machine learning methods' applications in futures market volatility prediction, which is helpful for academics, policy-makers, and investors.

Singlehanded or joint race? Stock market volatility prediction

This paper examines whether the realized skewness and kurtosis contain predictability for Shanghai Stock Exchange Sector Index. We find kurtosis contains more information to predict the Shanghai Stock Exchange Sector Index volatility. Importantly, the model considering the combination of both skewness and kurtosis has the best predictability for the stock market volatility. Moreover, we investigate the economic values of the models and asymmetric effects of skewness and kurtosis on stock market volatility, finding skewness (skewness <0) and kurtosis (kurtosis >3) own better forecasting performance. Finally, we discuss the predictability of skewness and kurtosis during two turbulent periods of China's stock bubble and the COVID-19 pandemic.

Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach

We propose a new multifractal volatility measure (MVA) and accordingly construct new types of multifractal volatility models. We compare these models to some alternatives, and consider long memory features, the benefits of leverage effects, and the presence of jumps. The analysis is applied to the multifractal volatility and realized volatility series of three representative indices, adopting six loss functions as guidance for the model selection. The forecast performance is evaluated and tested using model confident set (MCS) in both high- and low-volatility periods, as well as based on robustness checks on the continuous rank probability score statistics. The results show satisfactory performances for the new multifractal volatility models. Specifically, we find that our new multifractal volatility model significantly improves the one-day-ahead volatility forecasts in the high-volatility period. While in the low-volatility periods, the out-of-sample test results highlight the superiority of the traditional multifractal volatility models in the accuracy of volatility forecasting. These findings will be helpful for market volatility prediction and investment strategies.

RETRACTED ARTICLE: CSI300 volatility predicting by internet users’ searching behavior

Financial market volatility prediction has always been a hot topic in the field of financial mathematics. Inspired by the investor attention theory, many studies using internet users’ online searching behavior to forecast the volatility of financial market, In this paper, we apply ISOMAP-FCC-LSTSVM, FCC-LSTSVM, SVM and GARCH model by using 28 keywords which related to macro-economy and household consumption to predict the CSI300 volatility. Through the data experimental, we compare the accuracy of all the algorithms. Meanwhile, we also verify the conclusion of internet users’ search heterogeneity in our past research which was ignored in former studies.

Network of listed companies based on common shareholders and the prediction of market volatility

Abstract In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

A differential harmony search based hybrid interval type2 fuzzy EGARCH model for stock market volatility prediction

In this paper a new hybrid model integrating an interval type2 fuzzy logic system (IT2FLS) with a computationally efficient functional link artificial neural network (CEFLANN) and an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model has been proposed for accurate forecasting and modeling of financial data with changing variance over time. The proposed model denoted as IT2F-CE-EGARCH helps to enhance the ability of EGARCH model through a joint estimation of the important features of EGARCH like leverage effect, asymmetric shock by leverage effect with the secondary membership functions of interval type2 TSK FLS and the functional expansion and learning component of a CEFLANN. The secondary membership functions with upper and lower limits of IT2FLS provide a forecasting interval for handling more complicated uncertainties involved in volatility forecasting compared to type1 FLS. The performance of the proposed model has been observed with two membership functions i.e. Gaussian with fixed mean, uncertain variance and Gaussian with fixed variance and uncertain mean. The proposed model has also been compared with a few other fuzzy time series models and GARCH family models based on four performance metrics: MSFE, RMSFE, MAFE and Rel MAE. Again a differential harmony search (DHS) algorithm has been suggested for optimizing the parameters of all the fuzzy time series models. The results indicate that the proposed IT2F-CE-EGARCH model offers significant improvements in volatility forecasting performance in comparison with all other specified models over BSE Sensex and CNX Nifty dataset.

Volatility of stock market and capital flow

The study examines the relationship between capital flow, return of stock indices and exchange rate. This paper documents a significant relationship between capital flow and market volatility but returns of stock exchange and exchange rate are found to be insignificant. The study will be helpful for the policy makers so that they will be able to take the decision by considering the impact of capital flow. Because of the modern technology capital mobility financial and economic agent because it signify a measure increase with an accelerating role. Risk assessment of risk exposure in their investment. The capital flow plays analysis for the stock market after liberalization has an important role in this regard. Unstable stock market is assumed more importance in the world. Usefulness of an important concern for policy makers because instability efficient stock markets in dynamic resource is well known. of the stock market creates uncertainty and affects the Karachi Stock Exchange once again started moving in growth success. upward direction and performance of the economy of The purpose of the paper is to investigate with the Pakistan has increased. Investigating the behavior of aim of either the capital flow from the other countries volatility using a new set of variables is important. carries volatility with them or not. This study will be Stock market volatility is of vital important for helpful to investigate that volatility shocks from the other investors. Stock return usually follows the random countries will affect Karachi stock exchange and Colombo path and touches the maximum and minimum level. stock exchange. The volatility of stock price can be observed from its The second important purpose is to study with the fluctuation from the mean. Why we Study volatility in aim of either the shocks in the capital flow will affect the stock market and capital flows? volatility in the exchange rate or not. In latest years The capital flow may have different effects on volatility in equity market have turn out to be a topic of exchange rate and stock market volatility. joint worry for individual stake holders. Stock return Large capital inflows can damage the economy in volatility cause delay in economic performance during many ways. Capital flow raise asset bubbles and customer spending. Stock Return instability might too promote too much borrowing, but they also spoil the have an effect on trade asset expenses. Additional, the competitiveness of the export sector, they can also cause intense instability might upset the silky performance of a rapid increase in rates of exchange. Open account debit the monetary system and show the way to structural or of a country will build it yet further at threat to a quick method differences. setback of funds. On the other side, if central banks The stock market in Pakistan has had its fair interfere to seize down their currencies, the increase of contribute to trouble due to extreme speculation resulting funds can go in front to extremely loose financial in excessive volatility. Certainly, in the early 1990's the circumstances and getting higher inflation. confidence of investors to some extent has been eroded Stock market volatility prediction is imperative to