Daily Returns Research Articles

IMPACT OF DERIVATIVE WARRANTS INTRODUCTION ON THAILAND STOCK MARKET VOLATILITY

The Stock Exchange of Thailand (SET) first launched derivative warrants on SET50 index (SET50 DWs) on April 17, 2014. They are currently the most active DWs on the SET. This research uses the GARCH family models augmented with dummy variable to analyze the effect of SET50 DWs on stock market volatility. The sample data consist of daily returns of SET50 index from the period October 30, 2012 to December 30, 2019. The empirical results indicate that the coming into market of SET50 DWs reduces stock market volatility. The GARCH (1,1) TARCH (1,1), and EGARCH (1,1) models are not radically different from each other in their output. However, the asymmetric TARCH (1,1) model is found to provide the best fit in modelling volatility. The SET50 index shows the existence of leverage effect, where negative shocks have a greater impact on the volatility than positive shocks. Introducing SET50 DWs lowers the price volatility of SET50 index so investor having a portfolio investment with a correlation to the performance of SET50 index should adjust hedge ratio appropriately to manage investment risk. There is also a suggestion for policy makers to support the launch of DWs to lower the volatility in underlying spot market resulting in improved efficiency.

The safe haven feature of gold in the recent crisis

The global repercussions of the COVID-19 pandemic had a substantial adverse impact on the overall economy, resulting in equity markets decline worldwide. Amidst this turbulent environment, plagued with uncertainty, investors have redirected their attention towards secure assets to protect their capital. In that context the main objective is to reevaluate whether gold still demonstrates the qualities of a safe haven investment during unprecedented events like the COVID-19 pandemic and the Ukrainian war, events distinct from past crisis we have experienced for example 2008 and 1987. For the same reason, a rolling regression between gold and the S&P500 index was employed, taking into account the time-varying properties of daily returns and opting for an optimal window size. The findings indicate that significant negative slope coefficients coincide with the COVID-19 pandemic and the initiation of the Ukrainian war. These results offer empirical evidence that gold continues to maintain a robust position as a safe haven in the perception of investors. Nevertheless, it is essential to emphasize that there is no universally applicable safe haven; the safety features of different assets fluctuate over time, depending on the specific market conditions and the nature of financial turmoil.

A contagion test with unspecified heteroscedastic errors

The tests of contagion in Fry-McKibbin et al. (2010) filter returns by a vector autoregressive model, assume residuals are independent, fit a parametric distribution family to residuals, and test for the change of contagion measures, which ignore the effect of filtering the time series model and the stylized fact of heteroscedasticity in daily returns. This paper studies a contagion test based on correlation by allowing heteroscedastic errors with a deterministic jump from the pre-crisis to the crisis periods. Because the developed test does not infer heteroscedasticity, it is robust against heteroscedasticity but its asymptotic variance under the null hypothesis of no contagion becomes complicated, which relies on a block method for estimating the asymptotic variance. A simulation study confirms the good finite sample performance of the new contagion test. Finally, we apply the test to three datasets to test for contagion.

Empirical Analysis of SSE 50 Index Volatility Based on GARCH Model

Volatility is an important index for measuring the risk in the financial market. The research on the volatility of the financial market is the basis of risk prevention and asset pricing. The volatility of the market is predicted by using financial time series analysis and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This paper uses the 5 min closing price of the Shanghai 50 index from January 2021 to December 2021 as the small sample data, and the daily closing price of the Shanghai 50 index from January 2012 to December 2020 as the large sample data. The large sample data of SSE 50 index are divided into five sample groups with different interval lengths, and GARCH models are established to empirically analyze the volatility of its return. The “realized” volatility method is used to process the small sample data of the Shanghai Stock Exchange 50 index, and the estimated value of volatility is obtained as the substitute for the real value. The predicted value of five groups of data is compared with the real value, and the influence of sample size on the predicted value is analyzed. The results show that GARCH’s estimation coefficient is very significant and has the expected characteristics, which verifies the existence of persistent volatility aggregation, and the daily return fluctuation of the closing price of the Shanghai 50 index is largely affected by the news about volatility and lagging volatility in the previous period. This result shows that the daily return of the Shanghai Stock Exchange 50 index has sustained high volatility, the investment risk of the Shanghai stock market increases, and the regulatory authorities should take appropriate measures to prevent systemic risks.

Gaussian Bayesian network model of healthcare, food and energy sectors in the pandemic: Türkiye case

Healthcare, food, and energy are the basic needs of life in the globalizing world. Humankind's quest to maintain a healthy life and find ways to meet its needs has continued since its existence. The causal relations between the healthcare, food, and energy sectors are explored using data from the pandemic when COVID-19 was a global risk, and human health sustainability underwent a complicated process. It aims to examine the interaction between the healthcare, food, and energy sectors and model the causal relationship within the framework of probabilistic dependencies. For this purpose, the relationships between these sectors during the pandemic are modeled via Bayesian Networks (BNs). This highly successful inference method makes the complex structure of causal relationships graphically understandable. The data consists of stock returns at the end of the business day between March 11, 2020, when the pandemic was declared, and December 26, 2022. Data on 13 stocks actively traded on the Istanbul Stock Exchange (BIST) during the 700 days were obtained from tr.investing.com. Causal modeling uses Gaussian Bayesian Networks (GBNs) for continuous variables. To make the inferences drawn from the data more successful and minimize the loss of information, the GBN model is built with continuous variables. The posterior Probability Density Functions (PDFs) of the stocks in the network are constructed over the structure of the Directed Acyclic Graphs (DAG) of the BNs, and inferences are made by querying possible cases (tips). Markov Chain Monte Carlo (MCMC) simulations are performed with the posterior PDFs, and measures of the central tendency of the stocks are calculated. GBNs are used to generate daily return estimates for ULKER with the lowest MSE (1.06e-03) and RMSE (3.22e-02) values and ULUUN with the highest MSE (3.43e-03) and RMSE (5.83e-02) values.

Figuring Out Life After Covid-19: a Qualitative Study From Sweden.

To obtain a deeper understanding of the daily life experiences of working aged people during the year following hospitalization due to SARS-CoV-2 (COVID-19), with a focus on function-ing in daily life and return to work. An explorative qualitative study using individual interviews. A purposive sample was selected of persons who had received inpatient hospital care, had been discharged approximately 1 year previously and were of working age. Semi-structured interviews were conducted with 11 participants (9 men, 2 women). The interviews were transcribed and analysed with inductive thematic analysis. Four themes were identified. Navigating health, with or without support from healthcare, was described as challenging when managing consequences of COVID-19. Participants struggled with a lack of energy that interfered with daily life. It was a trial-and-error process trying to use familiar strategies in new ways to manage. The return to work process was facilitated by own strategies and support. This study contributes increased knowledge of everyday life experiences of people 1 year following hospitalization due to COVID-19. The lack of energy and a struggle to manage health while navigating the healthcare system emphasize the importance of strengthening personal and organizational health literacy to facilitate the recovery process after severe COVID-19.

COVID-19 Pandemic and Stock Market Contagion Between the Chinese Mainland and Hong Kong

This study utilizes stock market data from March 2020 to December 2022, employing VAR models and Granger causality models to investigate the daily returns and contagion effect of the CSI 300 and the HSI in Hong Kong. Based on this, the analysis explores the impact of the pandemic on the contagion effect between the mainland Chinese and Hong Kong stock markets. The research findings indicate that during the pandemic period, there exists a unidirectional Granger causality relationship from the Hong Kong stock market to the mainland Chinese stock market. The pandemic has intensified the connection between mainland China and Hong Kong, significantly increasing their mutual influence. The overall trends of the two stock markets are consistent, with the mainland Chinese market exhibiting smoother fluctuations during this period.

Dynamic conditional correlation between green and grey energy ETF markets using cDCC-MGARCH model

ABSTRACTUsing the cDCC form of the multivariate GARCH models (MGARCH), this paper examines the time-varying conditional correlations among green renewable, grey non-renewable, and the conventional investment strategy in the exchange-traded funds (ETFs) markets. Daily excess returns for the largest energy ETFs are used as proxies for the US energy sector over the period of 25 June 2008 to 9 May 2023. The empirical results find that the AR(1)-GARCH(1, 1)-cDCC model with t-distribution to be the best fit. The results indicate that the time-varying correlations between green and grey energy ETFs are between 0.42 and 0.55 and statistically significant at 10%, with lesser degree of persistence in green energy, while there is a high significant co-movement between the grey energy and the traditional investment strategy. This, in turn, implies that investing in green energy ETFs provides better diversification. These results provide important implications for policymakers, portfolio managers and investors on the benefits of portfolio diversification in energy markets amid the current global energy crisis.

High‐Frequency‐Based Volatility Model with Network Structure

This paper introduces a novel multi‐variate volatility model that can accommodate appropriately defined network structures based on low‐frequency and high‐frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi‐step‐ahead forecasting and targeting parameterization are discussed. Quasi‐likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures.

TESTING THE MARKET EFFICIENCY WHEN INTEREST RATES CHANGE: CASE IN INDONESIA

Until the end of September 2023, the interest rate policy in the Republic of Indonesia shifted from 3.50% to 5.75%. The objective of this study is to examine whether changes in interest rates have an impact on the market efficiency in Indonesia. The daily market returns of Indonesia Stock Exchange (IDX) indices from January 3rd, 2022, until October 9th, 2023 are employed. To examine the market efficiency in weak form, this study conducts the normality test by the Kolmogorov-Smirnov test, the unit root problem test by Augmented Dickey-Fuller test, and the variance ratio test. This study finds that most market indices experienced a decline in returns and were less volatile when interest rates shifted to higher levels. This study also finds that most market indices tend to remain efficient in weak form at both low and high interest rates. The finding implies that those indices tend not to be influenced by policy changes in interest rates. However, the findings also show that several market indices experience changes in conditions when interest rates shift. As the studies on the relationship between changes in interest rates and market efficiency are scarce, the novelty of this study is the use of event windows to get specific impact of changes in interest rates on market efficiency.

OPTIMAL HEDGE RATIO OF SUKUK AND ISLAMIC EQUITY: A NOVEL APPROACH

This research applies a novel model to compute a hedge ratio. Specifically, the model modifies volatility forecasts of an exponentially weighted moving average method to account for the fat-tailed distribution of returns. This simpler model aims to overcome the widely-known drawback of the complex GARCH models that a long daily return period is required to ensure the model’s convergence. The data are Islamic exchange-traded funds: SP Funds Dow Jones Global Sukuk ETF, Wahed FTSE USA Shariah ETF, and iShares MSCI EM Islamic UCITS ETF. Sukuk act as a diversifier over the turmoil period since they are positively correlated with Islamic equity and their volatility is less than that of Islamic equity. This work also implements widely-used methods such as Dynamic Equicorrelation-GARCH, GO-GARCH, asymmetric DCC-GARCH, naïve approach, and linear regression. Two forms of data splitting and a rolling-window analysis are carried out to reduce data mining bias. All models generate one-step ahead forecasts of hedge ratios. Applying wavelet-transformed returns and utility analysis incorporating third and fourth moments, the proposed models produce better performance than the competing models. The results remain the same irrespective of different hedging instruments (precious metals) and asset classes.

Research on the Volatility of the S&P 500 Stock Index Based on GARCH Family Models

Volatility is an important issue in financial research. In this paper, in order to study the volatility of the S&P 500 index, which is representative of the American stock market, is used as research object, and the daily closing prices from July 2018 to July 2022 are selected. It has since become widely accepted in the academic community that stock return volatility is slow moving. Therefore, the series cannot accurately capture the characteristics of volatility. Developing the GARCH model gives us an effective way of describing return volatility. The statistical software EViews is initially utilized to take the logarithmic difference of the data and obtain the daily return series of the past five years, and to test if the time series has ARCH effect. The results suggest that the returns in the series are not correlated with each other, and that volatility has an obvious correlation, which makes it suitable to model with a GARCH family model.

Stock Market Analysis using Long Short-Term Model

In today's world of value and improved investments, financial analysis has become a difficult task. The implementation of recurrent neural networks (RNN) and long short-term memory (LSTM) cells for stock market forecasting using time series of historical portfolio stock data is demonstrated in this study. In this study, we applied LSTM to predict stock market values using Yahoo Finance data along with Python modules Pandas and Matplotlib to evaluate the performance of the model. Our results show that the LSTM model is able to make accurate predictions of stock market prices and trends using historical data. The results of the correlation study showed a significant relationship between the daily return and the closing price of four randomly chosen companies. Overall, using LSTM, Yahoo Finance, Python Pandas, and Matplotlib modules to predict stock prices and provide useful information to investors was a successful strategy.

A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series

The literature on the extreme value theory threshold optimization problem for multiple time series analysis does not consider determining a single optimal tail probability for all marginal distributions. With multiple tail probabilities, their discrepancy results in a differing number of exceedances, which may favour a particular marginal series. In this study, we propose a single optimal tail probability by integrating trade-offs among multiple time series within an MOO framework. Mathematically, our approach links the peaks-over-threshold technique and goal programming technique by developing a set of regression functions, which represent continuous paths of possible tail areas for multiple time series, and we formulate them at the desired levels within a multiobjective optimization framework. The optimal solution is found as the minimum Chebyshev variant weighted value. Our approach advances the development of the peaks-over-threshold method by considering the characteristics of a group of time series collectively instead of independently. The proposed optimal tail probability can be considered an optimal reference point for practical risk investment portfolio analysis that employs an identical tail size across multiple time series data. The daily log returns of four U.S. stock market indices, namely, S&P 500, NASDAQ Composite, NYSE Composite, and Russell 2000, from 1 July 1992 to 30 June 2022 are studied empirically.

Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Interest rate market risk faced by China’s commercial banks is increasing after the announcement that the interest rate marketisation is completed. This paper examines the Value-at-Risk, and statistical properties in the daily price return of Shanghai banks’ overnight offered rate. The study applies two-stage approaches, combining GARCH-type models with extreme value theory. Firstly, the Markov regime switching model is used to test the regime states of the series. Secondly, the performance of different VaR models are examined. Results show that the extreme value approach estimates better at the 99% confidence level. The EGARCH-GED model is the most suitable of the employed GARCH-type models. The back-testing results support the idea that the approach used in this study is appropriate for improving commercial banks’ daily risk management. This paper applies the GARCH-EVT method for interest rate measurement after China’s interest rate marketisation and added regime analysis of interest rate. Suggested policy implications will help formulate policies that guide the activities of commercial banks in China.

The Effect of Rise of Federal Funds Rate on US Stock Market: From the Perspective of US Dollar Appreciation

The study examines the impact of the rise of the Federal Funds Rate (FFR) on the stock price returns and volatility of the United States of America. The study extracted the stock price from January 3, 2022, to June 16, 2023, from the Investing finance terminal, including the Standard and Poors (S&P 500), US Dollar Index, and the National Association of Securities Dealers Automated Quotations (NASDAQ) Index. This study uses the Vector Autoregression (VAR) model and the ARMA-GARCH model to evaluate the stock returns and daily return volatility, respectively. The two models inspect the effect of the US Dollar index on both the NASDAQ and S&P 500. The result of this study suggests that the rise in the Federal Funds Rate would cause the stock market price to decline for a relatively long period. However, the volatility of the daily return remains stable. By studying the effect of the rise in the Federal Funds Rate, policymakers can change the stock prices by adjusting the Federal Funds Rate based on the markets needs.

Mid-term clinical and sonographic outcomes of minimally invasive acromioclavicular joint reconstruction: mini-open versus arthroscopically assisted

IntroductionThe current literature describes various operative stabilization strategies which achieve good clinical outcomes after acute acromioclavicular joint (ACJ) dislocation. The aim of this study was to compare the mid-term clinical and sonographic treatment outcomes after minimally invasive mini-open and arthroscopic reconstruction.Materials and methodsWe conducted a retrospective two-center study of patients with acute ACJ dislocation. Surgical treatment was performed using either a mini-open approach (MIOP) or an arthroscopic technique (AR). The primary outcome parameters of this study were the sonographically measured acromioclavicular (ACD) and coracoclavicular distances (CCD). Secondary outcome parameters included the Constant–Murley score (CS), range of motion (ROM), postoperative pain scale (VAS), return to daily routine, return to sports, complications, as well as operative revisions.ResultsAfter a mean follow-up of 29 months, 30 patients were included in this study with an average age of 41.3 ± 14.8 years (MIOP) and 41.2 ± 15.4 years (AR). The sonographic ACD (MIOP 9.11 mm vs. AR 8.93 mm, p = 0.41) and CCD (MIOP 25.08 mm vs. AR 24.36 mm, p = 0.29) distances showed no statistically significant differences. Furthermore, there was no statistically significant difference when compared to the contralateral side (p = 0.42). With both techniques, patients achieved excellent clinical outcome parameters without statistically significant differences in CS (MIOP 95 vs. AR 97, p = 0.11) and VAS (MIOP 1.76 vs. AR 1.14, p = 0.18). The return to daily activity and return to sport rates did not differ. There were neither complications nor revisions in both groups.ConclusionBoth minimally invasive techniques for acute ACJ stabilization achieved excellent clinical and sonographic outcomes without one technique being statistically superior to the other.

Extreme Value Theory Modelling of the Behaviour of Johannesburg Stock Exchange Financial Market Data

Financial market data are abundant with outliers, and the search for an appropriate extreme value theory (EVT) approach to apply is an endless debate in the statistics of extremes research. This paper uses EVT methods to model the five-year daily all-share total return index (ALSTRI) and the daily United States dollar (USD) against the South African rand (ZAR) exchange rate of the Johannesburg stock exchange (JSE). The study compares the block maxima approach and the peaks-over-threshold (POT) approach in terms of their ability to model financial market data. The 100-year return levels for the block maxima approach were found to be almost equal to the maximum observations of the financial markets of 10,860 and R18.99 for the ALSTRI and the USD–ZAR, respectively. For the peaks-over-threshold (POT) approach, the results show that the ALSTRI and the USD–ZAR exchange rate will surpass 17,501.63 and R23.72, respectively, at least once in 100 years. The findings in this study reveal a clear distinction between block maxima and POT return level estimates. The POT approach return level estimates were comparably higher than the block maxima estimates. The study further revealed that the blended generalised extreme value (bGEVD) is more suitable for relatively short-term forecasting, since it cuts off at the 50-year return level. Therefore, this study will add value to the literature and knowledge of statistics and econometrics. In the future, more studies on bGEVD, vine copulas, and the r-largest-order bGEVD can be conducted in the financial markets.

Oil in crisis: What can we learn

This study focuses on the Chicago Board Options Exchange Oil Volatility Index (CBOEOVX)'s volatility transmission to the European stock markets. In the first section, to determine the contingent connection between market returns and CBOEOVX and to examine if CBOEOVX return Granger causes economic capital rates of return, the Dynamic Conditional Correlation (DCC) GARCH model has been used. Then, the study examined the asymmetric effects of fluctuations in estimated unpredictability on markets' profitability as the last step of the methoology. In this regard, the study applies quantile regression to examine the asymmetrical effect of the CBOEOVX on the market's daily returns. We have discovered a statistically significant inverse link that changes over time across our various sample markets. This hints at and supports CBOEOVX ‘s influence on the European stock market as a source of primary energy risk. In addition, we find substantial evidence of an asymmetric effect of fluctuations in the CBOEOVX on very negative market profits in the lowest quartile. Our findings not only build on and support previous research and add to the existing body of knowledge but also have evident consequences for future research, regulatory authorities, and practitioners. Practitioners and regulators may use these findings to better comprehend the connection between all crises and pave the path for the future.

Internal and External Determinants of Bank Syariah Indonesia Capital Gain during the Pandemic

This paper seeks to confirm and explain the relationship between internal factors of stock-lagged return and trading volume and external factors of composite price on returns of Bank Syariah Indonesia or BRIS. We collect the daily financial data of BRIS stock, Indonesia composite index, gold price, exchange rate, and Bank of Indonesia's deposit rate from the Refinitiv database. We obtained 1,230 time-series data to be analyzed further by implementing time-series analyses. Our research shows that the autoregressive integrated moving average (1,1,0) is the best model for univariate and multivariate analysis. Our study finds that lagged return has a negative effect on the bank’s daily return while trading volume has a positive impact on daily return. Our research reveals that composite price has the same effect as trading volume on daily return. Investors, especially short-term traders, may use our findings to formulate a different strategy for dealing with the volatility of Bank Syariah Indonesia stock. Our research contributes to the investment field by developing a new measurement for predicting the bank’s return when conducting short-term trading.