Daily Return Data Research Articles

The impact of Sino–US trade war on the co-movement between China’s stock market and global stock markets

Taking the Sino–US trade war as the background, we study the co-movement changes on horizontal return and volatility of the daily logarithmic return data of global stock indices from 2016 to January 23, 2020 from static and dynamic perspectives. Through static tools such as correlation coefficient matrices, complex networks, and Granger causality, it is found that the trade war has significantly strengthened the correlation between the Chinese stock market and the global stock market, and highlighted the position of the Hong Kong stock market. On dynamic analysis, the news impact surface shows the asymmetry of the co-movement for positive and negative news. The aDCC-EGARCH model is used to observe the dynamic co-movement changes between the Chinese stock market and the global stock market. It shows that trade conflicts have enhanced the co-movement between the Chinese stock market and the global stock market to varying degrees. At the same time, the Chinese stock market is strategically tilted towards the Asian market. Based on the research results in this paper, countries around the world need to pay more attention to the risk contagion between stock markets after the outbreak of the trade war. China’s inland markets should be alert to risk contagion from the Hong Kong market.

Shifts in Borsa Istanbul return behaviour: an analysis with change-point detection

Purpose- In Turkey, there is an enormous increase in local investors’ stock market interest for the last several years (Altınbaş, 2021: 36). Increasing inflation, deteriorating welfare, lack of alternative investments seems to be the foremost reasons of this attention. As of November 2022, Borsa Istanbul 100 (BIST 100) index value level reached to historic highs (tripled in one year) and this trend is expected to continue. The index is exponentially increasing in overall and there are several retracement periods over the time. What drives, strengthens or changes this behaviour of market? Do political developments, economic news or any other events influence investors? To answer these questions, change points in statistical parameters of market return distributions are examined and detected dates are evaluated. Analysis period covers the dates between January 2009 and November 2022. Methodology- Change points/breakpoints in statistical properties of time series can be informative for specific events that influence the behaviour (Beltratti & Morana, 2006; Chatzikonstanti, 2017; Eizaguirre, Biscarri, & de Gracia Hidalgo, 2004; Khuong Nguyen & Bellalah, 2008; Malik, 2011). In this study, change points determination is done with pruned exact linear time (PELT) method (Killick, Fearnhead, & Eckley, 2012). This method is applied to daily return data of the index, and it estimates a single or multiple points at which the statistical properties change. Analysis is conducted in R statistical software (R Core Team, 2021) with package changepoint.np (Haynes, Killick, Fearnhead, Eckley, & Grose, 2022). This is a modified version of original change point detection technique which is used in cases where there is no assumption on the statistical properties of observations (Haynes, Fearnhead, & Eckley, 2017). Findings- Many changepoints detected. Some of these coincide with explicit/significant events, such as coup attempt in July 2016 or Covid-19 pandemic announcements. Interestingly, frequency of change points increased over the last one and a half year and most of them are very close to Monetary Policy Committee meetings. But some changes seem not to be directly related with important events, and some important events did not significantly change market behaviour. Conclusion- Findings indicate it is neither possible to generalize market behaviour shifts with specific event types nor certainly expect a change after an event. Deeper analysis and interpretation of individual events and their impact may further provide further insights. Keywords: Stock market, investor sentiment, pruned exact linear time, change-point detection. JEL Codes: C22, G12, G14, G4

Mean-Variance and Single-Index Model Portfolio Optimisation:Case in the Indonesian Stock Market

Manuscript type: Research paper Research aims: This study aims to compare the performance of meanvariance and single-index models in creating the optimal portfolio. Design/Methodology/Approach: This study creates optimal portfolios using the mean-variance and single-index models with daily stock return data of 38 companies listed on the LQ45 index, IDX Composite index and Bank Indonesia’s 7-Day (Reverse) Repo Rate from January 1, 2012 to December 31, 2019. The two models are compared using the Sharpe ratio. Research findings: The result shows that the single-index model dominates the Indonesian Stock Exchange (IDX), more so than the meanvariance model. BBCA has the highest proportion for both mean-variance and single-index portfolios. Theoretical contribution/Originality: This study compares two popular portfolio models in the Indonesian stock market. Practitioner/Policy implication: This study helps investors to create optimal portfolios using a model that is more suited to the IDX. Research limitation/Implication: This study creates the optimal portfolio without differentiating risk preferences (i.e., risk averse, risk moderate and risk taker). In addition, this research only uses daily return data and does not compare it with weekly and monthly data.

Covid-19 Induced Herding: A Case of Sectoral Indices of The Indian Stock Market

Herding tendencies are likely to be more prominent during the crisis period. The Covid-19 outbreak has been nothing less than a crisis for the world. Assuming the possibility of Covid-19 induced herding among investors, this study attempts to examine the behaviour in the Indian stock market (S&P BSE) by analyzing the daily returns data of 10 indices and the S&P BSE Sensex using the CSAD model from January 01, 2015 to December 31, 2020, by dividing the period into three sets: whole period, pre Covid-19 outbreak period and during Covid-19 outbreak period. The complete analysis is done for three different states of the markets (general – overall market condition, bullish and bearish). The study finds the Indian stock market to experience sector specific herding behavior during Covid-19 period under the study, in general, as well as during bullish and bearish trends.

Are Sustainability Indices Infected by the Volatility of Stock Indices? Analysis before and after the COVID-19 Pandemic

Considering the growing importance of sustainable investments worldwide, we explore the volatility transmission effects between the EURO STOXX Sustainability Index and the stock market indexes of its stocks. Using daily index return data, during 2000–2022, covering the COVID-19 pandemic, Multivariate Generalized Auto-Regressive Conditional Heteroskedasticity (MGARCH) models are used to explore if volatility effects of the stock indices felt during the pandemic implied any evolution in the effects already felt between the volatilities existing in these stock indices and the effects of stock market indices’ volatility over the sustainability index. Results point to the great dependence that the sustainability index has on stock index movements. The volatility felt in stock indices during the pandemic period did not become decisive in reversing a previous correlation trajectory between the stock market and sustainability indexes. Provided that sustainability is not observed exclusively in financial and economic terms, but in a triple bottom line context (including the social and environmental sides), we should not verify a high influence of stock market indexes over the sustainability index, as the results point out. Policymakers and investors should be aware of the high influence and take measures to turn the sustainability index more independent.

A study of stock returns based on ARMA-GARCH model: Taking the CSI 300 Index as an example

This paper selects the daily return data of CSI 300 index from January 2, 2014 to September 30, 2020 to study the characteristics of CSI 300 index return volatility. An ARMA(3,3) model was fitted to the daily log returns of the CSI 300 index using R software, and the ARCH effect was found to exist. The returns were then fitted with an ARMA(3,3)-GARCH(1,1) model, the model fitting effect was tested, and finally the short-term returns of the CSI 300 index were predicted. The results of the empirical analysis show that the CSI 300 index return series has characteristics such as non-normal distributivity, spikes and thick tails, and aggregation.

Modelling volatility transmission in regional Asian stock markets

In this paper, we investigate the mechanism of volatility transmission in five regional equity markets in Hong Kong, South Korea, Malaysia, Singapore, and Thailand using the multivariate EGARCH method. Our sample consists of daily stock return data for 10 industries over the period from 14 July 2015 to 29 May 2020. The empirical estimates provide evidence of significant volatility spillover among Asian stock industries. Our model shows that Asian equity industries, tend to exhibit volatility clustering in the conditional variance, and this volatility clustering is asymmetric in nature. The estimations show that Hong Kong, South Korea, Malaysia and Thailand play a more important role in the transmission of financial volatility compared to Singapore. The empirical analysis provides no evidence of an asymmetric effect and volatility persistence in Singapore stock markets. Moreover, the variance of the return series is constant, suggesting weak co-movement of volatility in Singapore with other Asian markets. The immediate practical implication of these empirical findings is that regional investment can bring about potential benefits through risk reduction and portfolio diversification.

Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market

The primary objective of this paper is to empirically examine the nature and statistical significance of the news effect on conditional volatility of unpredictable components of stock returns. Daily stock return data of 12 local and multinational companies on Dhaka Stock Exchange Ltd., Bangladesh, for the period 1990 to 2011 were used in this study. The likelihood of asymmetric effects of news on conditional volatility was tested using a set of diagnostics under the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework. The results fail to reject the null hypothesis of symmetric effects, thereby suggesting that the conditional volatility of unpredictable components of stock returns is affected equally by positive and negative news. The robustness of the results was further checked by using three widely used asymmetric models, namely exponential GARCH (EGARCH), Glosten, Jagannathan & Runkle (GJR)-GARCH, and a partially non-parametric Autoregressive Conditional Heteroscedastic (PNP-ARCH) models. Yet again, the results do not provide any evidence of significant asymmetric effects in the volatility process. In addition, the descriptive results confirm the stylized facts of unpredictable return series such as non-normal distribution, time variant conditional volatility, and persistence in return volatility. Collectively these findings, perhaps, indicate the adequacy of the GARCH (1,1) model in representing the data generating process. A number of regulatory and behavioral factors that are anticipated to be accountable for the absence of asymmetric news effects are underlined. Finally, some policy implications of the results and possible extensions of the present paper are also conveyed. JEL codes: G10, G12, G14

GARCH-M MODEL AND THE BEHAVIOR OF RISK-RETURN RELATIONSHIP IN INDONESIA STOCK MARKET

This study examines the risk-return trade-off and volatility behaviour in Indonesia stock market. As the analytical tool this study uses GARCH-M model with symmetric GARCH(1,1). To obtain more reliable results, this study takes daily and weekly stock index as well as 5 individual stock returns from January 2004 to November 2020 as a sample. This study also investigates the results with two alternative mean equations, simple regression and AR(1) model. The first finding of this study is that in Indonesia stock market both in stock index and in individual stocks, the volatilities of return are time varying. From investigating the risk-return relationship the results are mixed. This study finds that positive risk-return relationships in stock market index are observed both in daily and weekly data. A positive risk-return relationhip in stock market index is also found either in AR(1) model of mean equation or in simple regression model. The same results are observed in two stocks investigated. There is one stock where a positive risk return relationship is observed only in daily return data not in weekly return data. A negative risk-return relationships is observed in one stock and there is no evidence of risk-return trade-off in one stock. The conclusion is that a positive risk-return relationship as a postulated by investment theory only exists in stock index and does not exist in all stocks
 
 Key words : Indonesia stock market, Risk-return trade-off, GARCH-M, GARCH(1,1), Time-varying volatility.

The compare of fitting between Markowitz model and single index model in different risk degree

COVID-19 outbreak impose a major threat on the economy. For such a major public event, government always take some measure to deal with them, and it will cause great impact to market. The paper tries to use 20 years of historical daily total return data for ten stocks, which belong in groups to three different sectors Internet Technology Industry, The financial industry, Consumer goods business, one (S&P 500) index (a total of eleven risky assets) and a proxy for risk-free rate (1-month Fed Funds rate). This paper will explore practical degree of MM model and IM model in different risk degree by choosing one market index and ten leading enterprises in all walks of life and try to use expect return to divide degree of risk. The research show that MM model will have better degree of fitting in medium risk degree, and IM will be more suitable for low-risk degree. Furthermore, when expect return above 40% to the high return level, the limiting condition will cause great influence.

STRATEGI INVESTASI BERBASIS RETURN PER UNIT RISK

This research is aimed at obtaining an industrial sector that has the ability to produce an Average Return Per Unit Risk (ARPUR) which is significantly better than the ability of other industrial sectors as a reference for implementing a return per unit risk-based investment strategy. In this study, 900 average return per unit risk data from each industrial sector index for the 2004 to 2018 observation period were used. The research data was generated from 35235 daily return data from all index sectors in the Jakarta Stock Industry Classification downloaded from the Bloomberg terminal. Data analysis was carried out using descriptive statistics, Analysis of variance and further tests. For investors who emphasize attention to the industrial sector with the best average ARPUR in determining the Return Per Unit Risk-Based Investment Strategy, attention can be paid to industrial sector 9 (JAKTRAD, Jakarta Trade, Services, and Investment) and industrial sector 6 (JAKPROP, Jakarta Construction, Property, and Real Estate) which ranks first and ranks second for the highest average ARPUR. However, based on a more comprehensive study, industrial sector 9 provides more comfort for investors because in addition to showing the highest average ARPUR performance, it also shows a lower dispersion than sector 6.

Stock Market Efficiency and COVID-19 with Multiple Structural Breaks: Evidence from India

The objective of the study is to investigate the influence of the coronavirus pandemic (endogenous crisis) on the stock market efficiency of India during the multiple break periods. The empirical analysis is performed using conditional heteroscedasticity and a small sample robust wild bootstrap automatic variance ratio test and automatic portmanteau test on a daily stock return data of two benchmark indices, that is, NIFTY and SENSEX. The empirical results demonstrate that the stock return of two indices deviates from market efficiency during some periods of the analysis, notably during the nationwide lockdown and peak periods of coronavirus cases in India. These findings indicate that changing stock market behaviour becomes more speculative and earns abnormal profits. To the best of the author’s knowledge, this study provides the first evidence of investigating the variations in the stock market efficiency of India in response to this endogenous crisis.

EVALUASI KOMPREHENSIF KINERJA RETURN PER UNIT RISK SEKTOR-SEKTOR INDUSTRI JASICA

This study analyzes the performance of return per unit risk of industrial sectors that are members of the JASICA index (Jakarta Stock Industrial Classification) index. The sample used is 900 data on average returns per unit risk from each industrial sector for the period 2004 to 2018, which is generated from 35235 daily return data from all sectors of the JASICA Index. Data obtained from the Bloomberg terminal. Data analysis tools used in this study include descriptive statistics, trend analysis, ANOVA and Tukey HSD Post hoc test. From descriptive statistics, it is found that sector JAKTRAD produces the highest average return per unit risk, followed by sector JAKPROP, sector JAKBIND, sector JAKCONS and sector JAKFIN, and so on. In descriptive statistics, sector JAKAGRI is the sector that produces the lowest average return per unit of risk. Further analysis of the trend of the average return per unit risk in the period of observation does not show any significant differences in the direction of movement between the performance of one sector and other sectors. The results of the ANOVA and Tukey HSD Post hoc tests only found that the average return per unit risk of Sector JAKAGRI was significantly different from the average return per unit risk of sector JAKPROP and sector JAKTRAD, while the other sectors did not show a significant difference.

A stock portfolio strategy in the midst of the COVID-19: Case of Indonesia

Stock price movements are interesting to discuss, because from these price movements investors will get capital gains. Problems arose, however, when Covid-19 hit the world, especially in Indonesia. The purpose of this study, then, is to determine whether there is a relationship and difference in return and risk between Economic Value Added (EVA) and Market Value added (MVA) portfolios in the Indonesian stock market. The sample used is 24 stocks with daily stock return data for the 2015-2020 period. The results of the study found something new, namely that there was a relationship and difference between returns and risks in the EVA and MVA portfolios in Indonesia. In addition, the research succeeded in forming EVA and MVA portfolios that exceeded market returns in Indonesia. The best strategy that investors can apply in investing is to use an active strategy, especially during conditions, such as the Covid-19 pandemic, which have an impact on high market fluctuation.

The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries

In this paper, we use the improved event study method to analyze the changes in the systemic risk trends of various financial sectors after the outbreak of COVID-19. The analysis is based on the daily return data of 45 Chinese financial institutions from January 2, 2019, to November 30, 2020. The improved event study method is also used to explore the horizontal, trend, and public opinion effects of the systemic risk. The empirical analysis results show that: (1) the occurrence of COVID-19 will increase the level and volatility of systemic risk in the financial industry. (2) After the outbreak of COVID-19, there is no horizontal effect in all financial industries. The banking and securities industries have significant and longer-lasting positive trend effects, and from the perspective of trend effects, in the face of external shocks, the banking industry is more stable than the securities industry. (3) After the outbreak of COVID-19, the banking and securities industries have a public opinion effect, which is gradually weakened; but there is no public opinion effect in the insurance industry.

PENGUKURAN TINGKAT RISIKO DAN KEUNTUNGAN SAHAM INDIVIDUAL DENGAN MENGGUNAKAN PENDEKATAN HISTORIS PADA METODE VALUE AT RISK (VaR) (STUDI KASUS TOP 10 SAHAM TERBAIK JANUARI 2022)

In research has been conducted to measure individual daily in January 2022 which is included in the Top 10 best stocks version of kontan.co.id risk and profit level using the Value at Risk (VaR) approach. The research method used to analyze the data refers to the Historical Simulation Model approach. The data used is the daily asset return data obtained from the daily closing price of shares during the January 2021 period. The results of the VaR method approach are Historical Simulation. The model shows CMPP stock with a risk of 91 million. The measurement results show that if the funds invested are Rp. 500,000,000.00 with a 95% confidence level, the period is 21 days.

An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100

Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.

The Effect of COVID-19 on the Relationship between Idiosyncratic Volatility and Expected Stock Returns

This study examines the effect of the COVID-19 pandemic on the relationship between idiosyncratic volatility and expected stock returns. Using daily stock return data in the US market from the Center for Research in Security Prices (CRSP), we estimate monthly idiosyncratic volatility and investigate the effect of the COVID-19 pandemic at the portfolio and firm level. The results of portfolio analysis and cross-sectional regression show that the relationship between idiosyncratic volatility and subsequent stock returns switches from negative to positive during the pandemic period. Furthermore, we find that the relationship is robust to skewness for the “before the pandemic” and “after pandemic” periods. On the contrary, when we control for the one-month return reversal, the effect of idiosyncratic volatility on the subsequent stock returns becomes insignificant in both periods. Therefore, the short-term return reversal effect is the underlying reason for the relationship switching from negative to positive in the pandemic period. Our results are beneficial for investors and researchers.

Analysis of efficient market anomaly on stock returns on Indonesia's composite stock price index and global stock price index

Market anomaly is an occurring phenomenon in the market. Supposedly, an anomaly does not exist in markets that are considered efficient. An anomaly is an aberration in the efficient market theory where existing information does not reflect stock prices; therefore, investors can earn abnormal returns. This study examines how The Day Of The Week Effect and The Month Of The Year Effect affect stock returns on the Indonesian Stock Price Index and the Global Stock Price Index. Samples in this study are daily stock return data and return data on stocks of IHSG, DJIA, SSEC, and N225. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to analyze data. The results show that in IHSG and SSEC, there was no The Day Of The Week Effect. The DJIA and N225 were found in The Day of the Week Effect. The Month of the Year Effect was found in IHSG, DJIA, SSEC, and N225.

Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions

Existing studies rely on exogenous drivers to improve the accuracy of the volatility forecasting of the least polluting fossil fuels, natural gas. However, the academic literature lacks a comprehensive analysis on the factors driving the volatility of clean energy stock indices, although the transition to renewable energy sources has resulted in the emergence of the clean energy sector as a fast-growing sector very appealing to environmentally responsible investors. This paper examines the ability of five uncertainty indices and seven global economic conditions to predict the realized volatility of clean energy stock and natural gas markets. Using daily return data of four exchange-traded funds to track the performance of the global clean energy stock market and natural gas prices, we construct the monthly realized volatility and apply various methods, including shrinkage methods. We find that both uncertainty indices and global economic conditions successfully predict clean energy realized volatility. The shrinkage methods consistently outperform dimensionality reduction methods and combination forecast methods for clean energy and natural gas. Whether clean energy ETFs or natural gas are used, the predictive information extracted from global economic conditions outperforms uncertainty indices using shrinkage methods. This suggests that real economic activities rather than text-based measures of uncertainty should be considered when investors and policy-makers analyze the volatilities of clean energy and natural gas. We further investigate the forecasting power of uncertainty indices and economic conditions in terms of variable selection, market conditions, and seasonality effects.