International Stock Market Indices Research Articles

Probability Distributions for Modeling Stock Market Returns—An Empirical Inquiry

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments.

Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

News-driven stock market index prediction based on trellis network and sentiment attention mechanism

Applying deep learning, especially time series neural network, to stock market prediction, has become one of the important applications in the quantitative finance field. However, due to the multi-correlation and volatility of the stock market, how to timely and accurately predict it has become a challenging issue. In order to cope with this challenge, a news-driven stock market index prediction model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) is proposed. A sentiment analysis model based on CNN and LSTM is presented to obtain the sentiment index of massive news crawled from authoritative financial websites. Furthermore, a sentiment attention mechanism is designed for data fusion of stock data and news sentiment index as the input of the simple and efficient TrellisNet network for model training and prediction. The performance of our model is systematically evaluated using seven major international stock market indices including S&P500, NYSE, DJI, NASDAQ, FTSE 100, Nikkei 225 and SSE, and comparative experiments demonstrate that SA-TrellisNet is competitive to the other state-of-the-art methods in predicting stock market indices.

Forecasting the variability of stock index returns with the multifractal random walk model for realized volatilities

We adapt the multifractal random walk model by Bacry et al. (2001) to realized volatilities (denoted RV-MRW) and take stock of recent theoretical insights on this model in Duchon et al. (2012) to derive forecasts of financial volatility. Moreover, we propose a new extension of the binomial Markov-switching multifractal (BMSM) model by Calvet and Fisher (2001) to the RV framework. We compare the predictive ability of the two against 10 classical and multifractal volatility models. Forecasting performance is evaluated out-of-sample based on the empirical MSE, MAE, and QLIKE criteria as well as using model confidence sets following the methodology of Hansen et al. (2011). Overall, our empirical study for 14 international stock market indices has a clear message: The RV-MRW is the best model throughout under the MAE criterion, i.e., for all indices and forecast horizons between one day and 100 days, with uniform results for forecast evaluations against both realized volatilities and squared returns or during tranquil/turbulent market periods. Based on the evaluation of MSE and QLIKE forecast errors, the RV-MRW, RV-BMSM, and RV-ARFIMA provide the most accurate forecasts during our tranquil sample from 2016–2018, where we can observe a transition from RV-MRW dominating long-term forecasts to RV-BMSM and RV-ARFIMA dominating in the short term. The new RV-BMSM takes the lead in phases of more turbulent market dynamics (sample 2010–2012), when it appears throughout in the 90% model confidence set at horizons ≤10 days and for 13 out of 14 indices at 20 days. These results are very promising if we consider that this is the first empirical application of the RV-MRW and RV-BMSM. Moreover, whereas RV-ARFIMA forecasts are often a time-consuming task, the RV-MRW stands out due to its fast execution and straightforward implementation.

The Co-Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

In the past two decades, especially after the financial crisis of 2007–09, the literature for examining the availability of integration between the stock exchanges in developed and developing markets has grown. The importance of this topic stems from the significant implications of the linkage between exchange markets on various decisions taken by interested parties, such as policymakers and investors, in the decisions for portfolio diversification. This study examines the relationship between a developing stock exchange index, Amman Stock Exchange Index (ASEI), and the number of international indices, including S&P 500, NASDAQ, Nikkei, DAX, CAC, and HSI for 2008-2019. To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network (ANN). Despite the results indicating that the ANN is more efficient than linear regression in investigating the availability of the relationship between ASEI and international indices, stepwise regression and neural network support this relationship. Furthermore, ANN results revealed that the S&P 500 index and year have the most substantial relationship with ASEI. Our research is theoretically and practically important; policymakers and investors can benefit from our findings. Future studies may explore the effect of different international stock market indices on ASEI or other developing markets. Further studies can use macroeconomic factors to build prediction models for stock market indices.

The ARMA-APARCH-EVT Models Based on HAC in Dependence Modeling and Risk Assessment of a Financial Portfolio

Multivariate modeling of dependence and its impact on risk assessment remains a major concern for financial institutions. Thus, the copula model, in particular Archimedean hierarchical copulas (HAC) appears as a promising alternative, capable to precisely capture the structure of dependence between financial variables. This study aims to widen the sphere of practical applicability of the HAC model combined with the ARMA-APARCH volatility forecast model and the extreme values theory (EVT). A sequential process of modeling of the VaR of a portfolio based on the ARMA-APARCH-EVT-HAC model is discussed. The empirical analysis conducted with data from international stock market indices clearly illustrates the performance and accuracy of modeling based on HACs.

Jump Contagion among Stock Market Indices: Evidence from Option Markets

This paper explores the contagious propagation of jumps among international stock market indices by exploiting a rich panel of stock and options data. We propose a multivariate option pricing model designed to allow for, but not superimpose, time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure employing a continuum of moments conditions in GMM with implied states. We introduce a partial-information approach to reduce the computational complexity arising in the multivariate setting, derive the asymptotic properties of our estimators, and analyze their finite-sample performance. Our empirical results reveal evidence of jump contagion in option markets, both from the US to Europe and vice versa, with the US leading the UK and standing on equal footing with Germany. We illustrate the importance of capturing jump contagion for risk management, option pricing, and scenario analysis.

The effect of COVID-19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics.

This study investigates the impact of COVID‐19 pandemic on stock returns, conditional volatility, conditional skewness and bad state probability. This study utilizes an asymmetric exponential generalized autoregressive conditional heteroscedasticity model to capture the asymmetric effect of positive and negative shocks (news) on conditional volatility. Using a sample consisting of international stock market indices in Brazil, China, Italy, India, Germany, Russia, Spain, United Kingdom, and United States, over the period from January 1, 2013 to December 31, 2020, we find unprecedented increases in conditional volatilities and bad state probabilities across all the markets. However, this impact is not symmetric across markets. Furthermore, we find that the negative affect of deaths is more pronounced, compared to the positive impact of recovered cases.

Do COVID-19 Epidemic Explains the Dynamic Conditional Correlation between China’s Stock Market Index and International Stock Market Indices?

This study presents an important view to the predictive capacity of COVID-19 for the correlation between Chinese stock market and 9 international stock market in Asia, Europe, and North America regions. In this paper, we try to investigate the spillover impacts of China’s stock market on the selected stock markets using an econometric methodology based on DCC-GARCH models during the period from May 01, 2019 to May 30, 2020. Our results show a strong significant DCC among China’s stock market index and selected international stock market indices especially in the outbreak of COVID-19. We find that the results related to the degree of the persistence of volatility, are sensitive to the existence of the COVID-19 surprises into the DCC-GARCH (1) model. We remark that that COVID-19 has a short-term impact on international stock market indices volatilities. Finally, we can conclude that COVID-19 explain the spillover impacts of China’s stock market index on selected countries using DCC-GARCH models. This paper gives an important framework to the investors to choose their portfolios in the financial markets especially in the crisis period. This paper contributes to the literature on assessing the impact of COVID-19 confirmed cases surprises on the correlation between China’s stock market index and selected international stock market in Asia, Europe, and North America regions.

Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.

Determinants and hedging effectiveness of China's sovereign credit default swaps

AbstractIn this study, we examine determinants and hedging effectiveness of China's sovereign credit default swaps (CDS) spread from January 2010 to April 2020. While both domestic and global factors are important variables that explain China's CDS in changes and levels, we find that global variables have more explanatory power compared to China's domestic economic factors. Analysis of Markov regime‐switching results demonstrate that MSCI Asia is a good indicator for China's CDS spread during tranquil state while gold is a more relevant predictor in the turbulent state. Our results further indicate that domestic variables determine China's CDS spread better than global variables during crisis period. Using dynamic conditional correlation (DCC) GARCH model, we show that China's CDS are a strong hedge and effective diversifier for domestic and international stock market indexes.

Long-Term Stock Forecasting

It is well-known that there is a strong correlation between valuation ratios and long-term returns on certain stock-market indices such as the S&P 500. Scatter-plots of valuation ratios versus long-term stock-returns often show a characteristic downwards slope, where higher valuation ratios correspond to lower future stock-market returns, and vice versa. But a formal explanation of this phenomenon has never been given until now. In this paper we show how to properly decompose stock-returns into three components: Dividend yield, change in valuation ratio such as the P/E or P/Sales ratio, and the change in Earnings or Sales Per Share. Together with the basic formula for calculating annualized returns, this explains the characteristic curves we often see in scatter-plots of long-term stock-returns. We also derive formulas that let us forecast the mean and standard deviation for the future stock-returns from these three components. This is demonstrated on real-world data for both individual stocks as well as entire stock-market indices such as the S&P 500, 400 and 600 for U.S. stocks, and various Exchange Traded Funds (ETF) for international stock-market indices.

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.

International linkages of Indian equity market: evidence from panel co-integration approach

The present study explores the linkages among Indian and six international stock market indices. High-frequency data (5 min) for a period of 1 year (June 2017–May 2018) are taken into account to assess short-term and/or long-term co-movement among seven world indices, namely NIFTY, DAX, DOW, CAC, EUROSTOXX, FTSE and S&P. The panel unit root test, panel co-integration test and panel Granger causality test are used to determine the association among selected markets. Results of the study provide evidence of significant co-integration among stock market indices in different countries. In the short term, the Indian stock market index is found to possess bidirectional causality with the US stock market indices, namely DOW and S&P. Additionally, no lead–lag relationship is found between NIFTY and the European stock market index. Findings of the study have important implications for investors, fund managers and portfolio investors.

Forecasting risk measures using intraday data in a generalized autoregressive score framework

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression set-up. We consider four intraday measures: the realized volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. In a forecasting study, the set of newly proposed semiparametric models are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized volatility measures, outperform the benchmark models consistently across all indices and various probability levels.

Multidimensional portfolio risk measurement: a mixed copula approach

It is an increasingly challenging task to explore the risk measurement for multidimensional portfolios with nonlinear correlative assets. A risk measurement scheme based on the mixed copula theory is proposed in this paper, where the mixed copula is constructed by the linear combination of three single Archimedean copulas, embodying greater flexibility than single copula in connecting different types of marginal distributions. In the scenario, ARMA-EGARCH model with t innovation is employed to fit marginal distributions, and the parameter values of the mixed copulas are inferred by maximum likelihood estimation (MLE) method, and interior point algorithm is used to calculate the extreme values of the MLE, VaR and CVaR, corresponding to the optimal portfolio with the minimum risk. Finally, an empirical study on five international stock market indexes in Europe is performed to verify the feasibility and effectiveness of the scheme.

Multidimensional portfolio risk measurement: a mixed copula approach

It is an increasingly challenging task to explore the risk measurement for multidimensional portfolios with nonlinear correlative assets. A risk measurement scheme based on the mixed copula theory is proposed in this paper, where the mixed copula is constructed by the linear combination of three single Archimedean copulas, embodying greater flexibility than single copula in connecting different types of marginal distributions. In the scenario, ARMA-EGARCH model with t innovation is employed to fit marginal distributions, and the parameter values of the mixed copulas are inferred by maximum likelihood estimation (MLE) method, and interior point algorithm is used to calculate the extreme values of the MLE, VaR and CVaR, corresponding to the optimal portfolio with the minimum risk. Finally, an empirical study on five international stock market indexes in Europe is performed to verify the feasibility and effectiveness of the scheme.

Measuring Asset Market Linkages: Nonlinear Dependence and Tail Risk

Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail risk. Motivated by these limitations, we introduce the (tail-restricted) integrated regression function, and we show how it characterizes conditional dependence and persistence. We propose simple estimates for these measures and establish their asymptotic properties. We employ the proposed methods to analyze the dependence structure of some of the major international stock market indices before, during, and after the 2007–2009 financial crisis. Monte Carlo simulations and the application show that our new measures are more reliable and accurate than competing methods based on MES or exceedance correlations for testing tail dependence. Supplementary materials for this article are available online.

Influence of ESGC Indicators on Financial Performance of Listed Travel and Leisure Companies

The objective of this work was to research the impact of environmental, social, governance, and controversies (ESGC) indicators on financial performance. We used a sample of financial and nonfinancial business data from international countries for 2017 obtained through the Thomson Reuters environmental, social, and corporate governance (ESG) database. The company participants in the study belong to the tourism sector and are listed on international stock market indices. The methodology used was based on parametric and nonparametric statistical tests. Evidence supports that governance practices significantly influence financial performance. The contribution of the study is two-fold—from a theoretical perspective, it adds to the existing literature and, from an empirical point of view, we developed ESGC indicators and their relationship with financial performance using a binary regression logistic model, with results that can be applied to an international tourist perspective.

Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework

A new framework for the joint estimation and forecasting of dynamic Value-at-Risk (VaR) and Expected Shortfall (ES) is proposed by incorporating intraday information into a generalized autoregressive score (GAS) model, introduced by Patton, Ziegel, and Chen (2019) to estimate risk measures in a quantile regression setup. We consider four intraday measures: the realized variance at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized variances. In a forecasting study, the set of newly proposed semiparametric models is applied to 4 international stock market indices: the S&P 500, the Dow Jones Industrial Average, the NIKKEI 225 and the FTSE 100, and is compared with a range of parametric, nonparametric and semiparametric models including historical simulations, GARCH and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized variance measures (especially at 5 minutes frequency), outperform the benchmark models consistently across all indices and various probability levels.