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After the Chinese government’s announcement to achieve the goal of “carbon peaking and carbon neutrality”, the solar photovoltaic power generation industry in the country has experienced rapid development. This paper examines the impact of China’s “dual-carbon” goal on the country’s photovoltaic industry. Photovoltaic companies listed on the stock market are selected and the stock price data of these selected companies from June 1, 2020 to January 28, 2022 are tested for potential structural changes. The paper adopts the Quandt–Andrews test and the results show that the four selected companies have indeed undergone structural changes within one year after the announcement of the dual-carbon goal, and that the demand for photovoltaic power generation equipment has increased significantly. Finally, some suggestions are put forward for enhancing the photovoltaic industry after the COVID-19 pandemic.

The research examines the effect of the COVID-19 outbreak on the dynamic co-movement of the stock markets of China, Japan, and the USA using a VAR model and a DCC-GJR-GARCH model. Especially, this study focuses on during and preceding the COVID period. This study data period extends from 1 January 2016 to 31 April 2022. The results demonstrate that COVID-19’s effect increases stock market volatility. Meanwhile, the VAR model revealed that the USA’s exogeneity was greater during COVID-19. In addition, the pre-crisis Granger Causality between China–USA and Japan–China is substantially higher than during the crisis. The findings of DCC-GJR-GARCH indicate the presence of volatility clustering in each of the stock markets. Moreover, the results suggest that the time-varying correlations between China and the USA during the pre-COVID period are greater than during the COVID period. The study’s findings highlight that investors attempting to increase investment diversification opportunities worldwide should always consider dynamic co-movement in different periods to maximize returns and minimize risk.

We discuss a model for simulating a long-time memory in time series characterized in addition by a stochastic variance. The model is based on a combination of fractional Brownian motion (FBM) concepts, for dealing with the long-time memory, with an autoregressive scheme with conditional heteroskedasticity (ARCH), responsible for the stochastic variance of the series, and is denoted as FBMARCH. Unlike well-known fractionally integrated autoregressive models, FBMARCH admits finite second moments. The resulting probability distribution functions have power-law tails with exponents similar to ARCH models. This idea is applied to the description of long-time autocorrelations of absolute returns ubiquitously observed in stock markets.

This study examines the return and volatility transmission/spillover between (Precious and Industrial) metals and stocks in the emerging Asian markets in the entire studying period and the two crisis sub-periods: the global financial crisis (GFC) and the Chinese Stock market crash sub-periods, and the normal sub-period that does not have any crisis. In addition, we estimate the optimal weights and hedge ratios for both metals and stocks. Employing the VAR-AGARCH model to estimate spillover, the results reveal the unidirectional return spillover from both precious and industrial metals to most of the Asian equity markets in the entire period as well as in the GFC and normal sub-periods but not the sub-period of the Chinese stock market crash. Besides, we reveal that there are unidirectional or bidirectional volatility transmissions between most of the precious metals and the Asian stock markets during the entire period and all the sub-periods. In contrast, the volatility spillover is not significant between most of the industrial metals and Asian stock markets during the entire period and all the sub-periods. On the other hand, our analysis on both optimal weight and hedge ratios suggests that adding nearly any metal to a portfolio of emerging Asian stocks improves its risk-adjusted return and helps to effectively hedge against stock risk exposure over both crisis and non-crisis sub-periods. Overall, these findings provide useful insights for portfolio diversification, asset pricing, and risk management.

We develop a general framework for applying the Kelly criterion to the stock market. By supplying an arbitrary probability distribution modeling the future price movement of a set of stocks, the Kelly fraction for investing each stock can be calculated by inverting a matrix involving only first and second moments. The framework works for one or a portfolio of stocks and the Kelly fractions can be efficiently calculated. For a simple model of geometric Brownian motion of a single stock we show that our calculated Kelly fraction agrees with existing results. We demonstrate that the Kelly fractions can be calculated easily for other types of probabilities such as the Gaussian distribution and correlated multivariate assets.

Following the 1997 financial crisis in East Asia, the issue of contagion has resurfaced. Contagion has most often been associated with high frequency events; hence, it has been measured on stock market returns, interest rates, the exchange rate, or linear combinations of them. This paper tests for evidence of contagion between selected East Asian stock markets, thereby exploring the importance of the linkages between stock markets as a transmission channel during the crisis.

Most of works on stock price forecasting are concerned with the problem of predicting its future value. However, forecasting stock price future fluctuation trend could be easier and interesting for traders and investors to maximize profits. The purpose of this study is to predict CAC40, FTSE, NASDAQ and S&P500 price index up and down fluctuations. In particular, it aims to propose a methodology to forecast regime switches in these markets time series to assist traders and investors in decision making. In the first stage, a large set composed of twenty five technical analysis indicators is formed. They fall into four broad categories namely oscillators, stochastic measures, indexes and indicators. Entropy statistic is employed to rank the initial technical analysis indicators. Finally, in the third stage, polynomial-based kernel support vector machines (SVM) are used for predicting CAC40, FTSE, NASDAQ and S&P500 future upward and downward fluctuations. The forecasting results show that the choice of technical analysis indicators used to predict CAC40 and NASDAQ fluctuations depend on the type of risk-aversion and risk-appetite of the investor. For the S&P500 and FTSE, technical analysis indicators used in our study can detect future downshifts with high accuracy. Thus, they are suitable for market analysis and trading by risk-averse investors on these markets.

In this paper, we employ multiscale cross-sample entropy (MSCE), multiscale detrended cross-correlation analysis (MSDCCA) and DCCA cross-correlation coefficient (σDCCA) measurement to investigate the relationship between time series among different stock markets. We report the results of synchronism and cross-correlation behaviors in US and Chinese stock markets by these three methods. It can be concluded that the MSCE analysis point out the similarity on the cross-correlation among the stock markets while the MSCE makes it difficult to distinguish the indices in the same region and identify the difference and uniqueness of stock markets. However, both the MSDCCA analysis and σDCCA analysis reflect the similarity and uniqueness on the cross-correlation behaviors and reach the consistency. Furthermore, MSDCCA gives detailed multiscale cross-correlation structures and show some new interesting characteristics and conclusions, while the multiscale analysis by σDCCA provides a large amount of information on the cross-correlations and quantifies the level of cross-correlation more clearly and intuitively. MSDCCA and σDCCA methods may be more proper measures for the investigation of the cross-correlation between time series. We believe that such researches are relevant for a better understanding of the stock market mechanisms, and may lead to a better forecasting of the stock indices.

The connection between Singular Spectrum Analysis (SSA) decomposition and short-term market movements is investigated. Since SSA is a non-parametric approach, suitable to decompose general time-series into meaningful components, such as trends, oscillations and noise, it is proposed as a new oscillator-type Technical Indicator, replacing popular ones. New Technical Trading Rules (TTRs) are designed and applied to some major global stock indexes to illustrate the benefits in terms of revealing market movements. The performance is evaluated according to different risk-adjustment metrics and the empirical results reveal that the SSA-TTRs may outperform some popular technical oscillators and also the Buy & Hold strategy.

In the past, it was believed that investors may generate abnormal returns (AR) for trading stocks by employing technical trading rules. However, since the COVID-19 pandemic broke out, stock markets around the world seem to suffer a serious impact. Therefore, whether investors can beat the markets by applying technical trading rules during the period of COVID-19 pandemic becomes an important issue for market participants. The purpose of this study is to examine the profitability of trading stocks with the use of technical trading rules under the COVID-19 pandemic. By trading the constituent stocks of DJ 30 and NASDAQ 100, we find that almost all of the trading rules employed in this study fail to beat the market during the COVID-19 pandemic period, which is different from the results in 2019. The revealed findings of this study may shed light on that investors should adopt technical trading with care when stock markets are seriously affected by black swan events like COVID-19.

The objective of this paper is to introduce the decision-making model for stock markets. The proposed model is based on the study of historic data and the application of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) algorithm. In the proposed decision-making model the ANN are applied in order to make the analysis of historical daily stock returns and to calculate the recommendations concerning the purchase of the stocks. Subsequently, the application of PSO algorithm is made. The core idea of this algorithm application is to select the "global best" ANN for future investment decisions and to adapt the weights of other ANN towards the weights of the best network. The experimental investigation results presented in this paper show the potentiality of PSO algorithm applications for the decision-making in the stock markets.

The intention of this article is to develop an instrument to overcome the limitations caused by traditional analyses and present a combined STR — Smooth Transition Regression model (EGARCH, STRIGARCH, and STR-FIEGARCH) to analyze the contagion effects of the 2008 financial crisis. The proposed instrument will aid the analysis of contagion and the impact of changes in long-term interest rates on the returns of international stock indices and forecasting, with special emphasis on the effects caused by structural breaks, persistence, and conditioned heteroscedasticity. The methodology begins with unit root tests with one and two structural breaks. In the second step, the asymmetry will be analyzed considering the STR models, which will determine the asymmetry relationship between interest rates and the long term, so that in a later step, these asymmetries will be used in the composition of a volatility estimation model, being based on the ARCH models: (i) EGARCH and (ii) FIEGARCH. This study provides a useful instrument based on modeling techniques to make the decision-making process more efficient and objective, providing a choice of instruments that assess the effect of changes in interest rates on stock market indices when influenced by falls, with structural data and better forecasting performance. The results show that the developed mixture models obtained better performance in predicting the effect or impact of changes in interest rates on stock market indices when influenced by structural breaks. STR and the ARCH family are useful instruments that make the decision-making process clearer and more objective when choosing instruments that assess the spillover effect of long-term interest rates on the profitability of international financial indices.

This study examines the existence of interrelationships among GCC and Jordanian stock markets. The study applied weekly data for stock price indices for the Amman Exchange (ASE) and Gulf Cooperation Council (GCC) countries, namely, Saudi Arabia, Qatar, Bahrain, Kuwait, Oman and the United Arab Emirates. In this analysis, weekly market returns were employed to avoid the overlapping of recognized holidays and trading hours in the GCC and the ASE. During the global financial crisis and the COVID-19 pandemic crisis, the sample period spanned from January 7, 2009 to December 30, 2020 for overall (626) weekly observations The main contribution of this study is to shed light on the dynamics of cross-market associations, particularly in the context of emerging economies can be concluded as follows: First, this study is a forefront study that tested the linkages between GCC and Jordanian Markets. Second, the period of this study covered various crises such as the global financial crisis, and COVID-19. Third, the empirical study of multivariate integration, which considers both pairwise interactions and the overall integration of different stock markets by utilizing cutting-edge econometric methods like VECM and DCC-GARCH. As a result, an examination of ASE’s weekly stock price index was conducted. The findings show that there is causality and correlation among stock market indices. Three unidirectional causality relationships running from KUW, OMAI, and UAEI to ASEI. Moreover, the results revealed two bidirectional causality relationships, one between ASEI and BAHI and the other between ASEI and UAEI. The conditional correlations appear to be generally negative, implying that higher ASE volatility causes worse GCC stock returns. Saudi Arabia has the highest conditional connections, followed by Bahrain, Qatar and the United Arab Emirates, while Kuwait and Oman’s stock markets are the least connected with the ASE among their peers. Since financial indices are essential for investors, portfolio managers, risk management and fiscal and monetary policy, the findings of this study may be interest of many parties of them, especially for investors, policymakers and governments.

Based on daily data from Thomson Reuter’s Refinitiv, we investigate the effect of information and communication technology (ICT) on the profitability and risk of the European banking industry during the COVID-19 pandemic. Specifically, we empirically examine whether and how ICT diffusion affects banks’ stock return, credit default swaps (CDS) spreads and market volatility over the period spanning from January 1, 2020, to March 31, 2020. Our evidence demonstrates that ICT improves banks’ performance measures and reduces risks. These effects are more significant in the post-COVID-19 period.

This paper explores financial spillovers between emerging Asia and advanced economies using principal component analysis to extract common shocks in Asia. We first investigate stock market spillovers across the regions and find that spillovers from emerging Asia became significant after the global financial crisis. However, our industry-level analysis shows that the increased spillovers can be attributed to the first principal component (PC) in the manufacturing sector rather than to the first PC in the financial sector. This implies that the rise of the Asian manufacturing sector in the global market played a key role in enhancing the stock market spillovers. We next examine bilateral spillovers in short-term and long-term rates. In the tapering period, we find significant spillovers in long-term rates from the first PC in emerging Asia to Europe and the United States. However, these spillovers were much smaller than the stock market spillovers in magnitude.

This paper re-examines the role of the euro in enhancing the convergence among stock markets of Germany, France and Italy. The VAR-framework is implemented with a dummy variable. A pre- and post-euro analysis is also performed with an exclusion of the dummy variable. Monthly data on stock market returns from February 1994 through December 2007 are employed. The empirical results reveal evidence of significant convergence among the above markets during the post-euro era relative to the pre-euro era with net positive short-run causal effects.

In this chapter, we examine risk spillover between the returns series of oil and stock prices of worst-affected countries due to the COVID-19 outbreak in unconditional and conditional frameworks, where the relationship was conditioned upon the US economic policy uncertainty and financial stress indices. Specifically, we used three different measures of oil prices, namely, WTI, OPEC, and Dubai oil prices. We also examined the risk spillover from US and Chinese stock markets to stock markets of affected countries, such as the UK, France, Germany, Italy, Spain, Switzerland, and Turkey, for the time period from 31st December 2019 to 22nd April 2020. Our results provide evidence that during the COVID-19 outbreak, Dubai and OPEC oil prices have had a strong positive effect on stock price when both of them are at their lower quantiles, which suggests that during extreme markets conditions, oil price affects stock price. Furthermore, evidence of the directional predictability from stock returns of the US/China to all other stock returns shows positive predictability from the US to France, Germany, Italy, Spain, Switzerland, and the UK at lower quantiles. Last but not least, when the relationships were conditioned by the policy uncertainty and financial stress, evidence of directional predictability became stronger and spread to more quantiles, suggesting that the interrelationships between oil price and stock price returns and between stock price returns of the US/China to all other stock price returns were not driven by the systemic risk but rather uncertainties during the COVID-19 outbreak.

The Fed model postulates that the equity earnings yield follows the bond yield in the long run. Our tests based on a cointegration analysis of the United States, United Kingdom and German data indicate that the Fed model has predictive power in forecasting changes in the equity prices, earnings and bond yields. The predictions are better in the US than in other countries. Our approach consists of building a Vector Equilibrium Correction model which provides a quantitative dynamic version of the Fed model.