scholarly journals Spillover effects in the financial year cycle for Indian markets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Parul Bhatia
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Year Effect ◽  
Autoregressive Conditional Heteroskedasticity

PurposeThe stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.Design/methodology/approachThe oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.FindingsThe findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.Research limitations/implicationsThe study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.Originality/valueThe paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

Modeling volatility on the Karachi Stock Exchange, Pakistan

2016 ◽  
Vol 10 (3) ◽  
pp. 253-275 ◽  
Author(s):  
Shahan Akhtar ◽  
Naimat U. Khan
Keyword(s):  
Financial Crises ◽  
Stock Exchange ◽  
Small Sample ◽  
Leverage Effect ◽  
Data Series ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Modeling Volatility ◽  
The Impact

Purpose The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods. Design/methodology/approach This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model. Findings The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels. Originality/value Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmed Jeribi ◽  
Achraf Ghorbel
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Value At Risk ◽  
Developed Countries ◽  
The Other ◽  
Conditional Heteroskedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Developed Stock Markets ◽  
Generalized Orthogonal

PurposeThe purpose of this paper is threefold. First, it models and forecasts the risk of the five leading cryptocurrencies, stock market indices (developed and BRICS) and gold returns. Second, it conducts different backtesting procedures forecasts. Third, it focuses on the hedging potential of cryptocurrencies and gold.Design/methodology/approachThe authors used the generalized autoregressive score (GAS) models to model and forecast the risk of cryptocurrencies, stock market indices and gold returns. They conduct different backtesting procedures of the 1% and 5%-value-at-risk (VaR) forecasts. They also use the generalized orthogonal generalized autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of cryptocurrencies by estimating the dynamic conditional correlation between cryptocurrencies and gold, on the one hand, and stock markets on the other hand.FindingsWhen conducting different backtesting procedures of VaR, our finding suggests that Bitcoin has the highest VaR among cryptocurrencies and Gold and the BRICS indices returns have lower VaR compared to the developed countries. Finally, we provide evidence that the risks among developed stock markets can be hedged by Bitcoin and Gold. Bitcoin can be considered as the new Gold for these economies. Unlike Bitcoin, Gold can be considered as a hedge for Chinese and Indian investors. However, Gold and Bitcoin can be considered as diversifier assets for the other BRICS economies while Dash and Monero are diversifier assets for developed stock markets.Originality/valueThe first paper's empirical contribution lies in analyzing optimal forecast models for cryptocurrencies (other than Bitcoin) returns and risk. The second contribution consists of studying the hedging potential of five leading cryptocurrencies. To the best of our knowledge, no previous studies have investigated the role of cryptocurrencies for BRICS investors.

A quantile regression approach to evaluate the relative impact of global and local factors on the MENA stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slah Bahloul ◽  
Nawel Ben Amor
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Stock Markets ◽  
Financial Integration ◽  
Ordinary Least Squares ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

PurposeThis paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.Design/methodology/approachThe authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.FindingsThe results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.Originality/valueThis paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

scholarly journals Measuring Dynamics of Risk and Performance of Sector Indices on Zagreb Stock Exchange

2015 ◽  
Vol 1 (1-2) ◽  
pp. 27-41
Author(s):  
Tihana Škrinjarić
Keyword(s):  
Portfolio Selection ◽  
Capital Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Conditional Heteroskedasticity ◽  
Market Sector ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Illiquid Market ◽  
And Performance

Abstract Investors are interested in sector diversification on stock markets among other important portfolio topics. This paper looks at five sector indices on Croatian capital market as an example of a small, relatively illiquid market. Sector indices have been constructed at the beginning of 2013 and since then there is a lack of studies, which focus on sector diversification on Zagreb Stock Exchange (ZSE). Thus, the purpose of this paper is to evaluate the recent dynamics of risk and performance of five sector indices on ZSE by employing MGARCH (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models empirically. Output from the analysis is used to form guidance for investors on Croatian capital market. The results indicate that in the observed period from February 4th 2013 to October 13th 2015 portfolios based on MGARCH methodology outperform other portfolios in terms return and risk. Thus, it is advisable to use this methodology when making portfolio selection.

scholarly journals A Study on the Volatility of the Bangladesh Stock Market — Based on GARCH Type Models

2017 ◽  
Vol 5 (3) ◽  
pp. 193-215 ◽  
Author(s):  
Bhowmik Roni ◽  
Chao Wu ◽  
Roy Kumar Jewel ◽  
Shouyang Wang
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Risk Premium ◽  
Leverage Effect ◽  
Conditional Heteroskedasticity ◽  
Model Accuracy ◽  
Risk And Return ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Changes Over Time ◽  
Over Time

Abstract The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant “low-yield associated with high-risk” phenomenon is detected in the crisis period and the “leverage effect” occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

Tunisian revolution and stock market volatility: evidence from FIEGARCH model

2015 ◽  
Vol 41 (10) ◽  
pp. 1112-1135 ◽  
Author(s):  
Ahmed Jeribi ◽  
Mohamed Fakhfekh ◽  
Anis Jarboui
Keyword(s):  
Stock Market ◽  
Financial Services ◽  
Stock Exchange ◽  
Leverage Effect ◽  
Stock Market Volatility ◽  
Political Uncertainty ◽  
Content Type ◽  
Stock Indices ◽  
Tunisian Revolution ◽  
The Impact

Purpose – Previously elaborated research works, dealing with the political uncertainty effect on stock market, have been primarily concerned with such political events as terrorist attacks, elections, wars, natural catastrophes and financial crashes. Such little research has been concerned with civil uprisings and revolutionary movements, as crucial sources of political uncertainty. The purpose of this paper is to study the impact of political uncertainty (resulting from the Tunisian Revolution) on the volatility of major sectorial stock indices in the Tunisian Stock Exchange (TSE). Design/methodology/approach – The authors apply the fractionally integrated exponential generalized autoregressive conditional heteroscedasticity model (FIEGARCH), which helps maintain a direct shock-persistence as well as a shock asymmetric volatility measurement. This model is applied to the daily returns relevant to nine sectorial stock indices and to the Tunisian benchmark index (TUNINDEX) with respect to three sub-periods (before, during and follows the Tunisian Revolution). Findings – The reached findings suggest that the shock impact throughout the Revolution period on construction, industries, consumer services, financial services, financial companies indices’ sectorial and the TUNINDEX return volatilities have proven to be permanent, while its persistence on the other indices has been discovered to be transitory. In addition, the achieved results appear to reveal a low leverage effect on all indices. This result seems to be very important since the Tunisian Revolution turns out to have a very important effect on the TSE. Originality/value – The paper’s empirical contribution lies in using the FIEGARCH approach to model the Tunisian sectorial indices’ volatility dynamics, persistence degree and leverage effect. This contribution goes a long way in helping regulators and international investors to further recognize the extent to which political instability does participate in affecting the TSE.

scholarly journals HOW VOLATILE IS INDIAN STOCK MARKET? A STUDY BASED ON SELECTED SECTORAL INDICES

2015 ◽  
Vol 3 (12) ◽  
pp. 142-149
Author(s):  
Nishad Mohamed ◽  
K.T. Thomachan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Conditional Heteroskedasticity ◽  
Stock Indices ◽  
Highly Sensitive ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Egarch Model ◽  
Indian Stock Market

This paper examines the nature of volatility of selected sectoral stock indices traded in the National Stock Exchange. Using the EGARCH model introduced by Nelson, it has been observed that the selected indices are subject to Autoregressive Conditional Heteroskedasticity (ARCH) effects. There are significant leverage effects in the case of five indices. Volatility seems to be highly persistent in the case of all the indices except one. Moreover, four indices are highly sensitive to market events.

Impact of political events on stock market returns: empirical evidence from Pakistan

2014 ◽  
Vol 30 (1) ◽  
pp. 60-78 ◽  
Author(s):  
Mian Sajid Nazir ◽  
Hassan Younus ◽  
Ahmad Kaleem ◽  
Zeshan Anwar
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Market Returns ◽  
Government Structure ◽  
Stock Market Returns ◽  
Content Type ◽  
Noisy Information ◽  
Political Events ◽  
Study Methodology

Purpose – The purpose of this paper is to investigate the relationship between uncertain political events and Pakistani Stock Markets from May 1999 to December 2011. Design/methodology/approach – Using the mean-adjusted return model and event study methodology and by comparing the market efficiency between the two government style, i.e. autocratic and democratic, the authors determined that how uncertain political events are affecting Pakistani Stock Markets. Findings – The empirical result shows that political events have an impact on the Karachi Stock Exchange (KSE) returns. Moreover, the paper derives from the results that the KSE is inefficient for a short span of time, after 15 days KSE absorbs the noisy information. The political situation in Pakistan was more stable in autocratic government structure than in democratic structure but it is difficult to state that the stock markets are more efficient in Autocracy because only few events took place during an autocratic regime and magnitude of events was not same in the autocratic and democratic government structure. Originality/value – This study is unique in its nature as it examines the effect of multiple political events on stock market returns in Pakistan simultaneously and is expected to contribute significantly in the capital market literature of Pakistan in particular.

Layoffs and stock market performance during the COVID-19 pandemic: evidence from the US

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christos Floros ◽  
Maria Psillaki ◽  
Efstathios Karpouzis
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Trading Volume ◽  
Negative Impact ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Content Type ◽  
Stock Market Performance ◽  
Event Date

PurposeThe authors examine the short-term stock market reaction surrounding US layoffs during the coronavirus disease 2019 (COVID-19) period. The authors’ specific interest is on any changes that may be observed in US stock markets during the COVID-19 outbreak. This information will help us assess the extent to which policymakers adopted at time revenue and expenditures measures to minimize its negative impact.Design/methodology/approachThe authors study the linkage between layoffs announced by firms and stock markets in US for the COVID-19 period between March 2020 and October 2020. This period shows important economic figures; a huge number of job cuts announced by blue-chip companies listed in the New York Stock Exchange (NYSE) due to widespread economic shutdowns. The authors examine whether and to what extent stock markets in US have reacted to layoff announcements during the COVID-19 pandemic using an event-study methodology.FindingsThe study’s results show that US layoffs during the pandemic did not cause any abnormalities on the stock returns, either positive or negative. Based on the mean-adjusted volume, the authors find that layoffs increase the stocks' trading volume, especially on the event date and the day following the event. US stocks become more volatile on the days following the event. Interestingly, on the event date, the authors find that stocks get the highest abnormal volatility; however, the result is statistically insignificant.Practical implicationsThe authors suggest that layoffs announcements follow the business cycle quite closely in most industries. The study’s results have implications for investors, regulators and policymakers as they permit to examine the effectiveness of the measures adopted.Social implicationsThe study’s results show that policymakers reduced uncertainty implementing intensive measures quickly and should follow similar policy in the future pandemic and/or unexpected events.Originality/valueThis paper contributes to the literature in two directions: First, to the best of the authors’ knowledge this is the first study that provides empirical evidence and assesses the extent to which a major global shock such as the COVID-19 pandemic may have altered the reaction of US stock markets to layoff announcements. Second, this is the first study on this topic that examines volume and volatility abnormalities, while the authors check the robustness of the findings with different methods to calculate abnormal returns.

Momentum Effect in Stock Market: Empirical Evidence from Pakistan Stock Exchange

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Shahid Rasheed ◽  
Umar Saood ◽  
Waqar Alam
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Stock Markets ◽  
Stock Exchange ◽  
Momentum Effect ◽  
Using Data

This study aims to examine the momentum effect presence in selected stocks of Pakistan stock market using data from Jan 2007 to Dec 2016. This study constructed the strategies includes docile, equal weighted and full rebalancing techniques. Data was extracted from the PSX – 100 index ranging from 2007 to 2016. STATA coding ASM software was used for calculating momentum portfolios, finally top 25 stocks were considered as a winner stocks and bottom 25 stocks were taken as a loser stocks. In conclusion, the results of the study found a strong momentum effect in Pakistan stock exchange PSX 100- index. As by results it has been observed that a substantial profit can earn by the investors or brokers in constructing a portfolio with a short formation period of three months and hold for 3, 6 and 12 months. There is hardly a study is present on the same topic on Pakistan Stock Exchange as preceding studies were only conducted on individual stock markets before merger of stock markets in Pakistan while this study leads the explanation of momentum phenomenon in new dimension i.e. Pakistan Stock Exchange. Keywords: Momentum, Portfolio, Winner Stocks, Loser Stocks

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