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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.

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.

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.

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.

The aim of this study is to compare volatility persistence with daily volatility and to analyze the asymmetry effect of volatilities in stock markets of emerging economies. Using daily observations of stock market indices of selected major emerging countries during the period of January 1, 2002 to December 31, 2018, the authors estimate the persistence, the half-life measure of volatility and the daily volatility of the return series using the GARCH model application. The authors also examine the leverage effect on stock market returns using the EGARCH model estimation. In addition, the authors investigate the impact of the 2008 global financial crisis on various volatility measures and the leverage effect of emerging stock market returns. The authors then examine and compare the different speeds of mean reversion, volatility persistence and leverage effects in the national stock market indices during the pre-crisis, crisis, and post-crisis periods. The authors hereby present evidence that the effects of negative shocks are significantly larger than those of positive shocks in emerging stock markets throughout their different sample periods.

The 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.

The 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.

The 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.

The 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.

The 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.

Several studies focus on asymmetric impact of shocks on conventional stocks. However, only few studies explore Islamic stocks, but none has examined the asymmetric impact of shocks on Islamic stocks. This study aims to fill the gap by investigating the asymmetric impact of shocks on Islamic stocks. Specifically, it identifies the effect of good and bad news on Islamic stock market. The study also aims to examine the returns and volatility spillover effects across different Islamic markets.

To carry out the empirical analysis, the authors have applied the exponential generalized autoregressive conditional heteroscedasticity (ARCH) model on daily Islamic stock indices of 18 countries. The study covers the period from July 2009 to July 2016. The authors have started their empirical analysis by examining the time series properties and testing the presence of ARCH effects. Further, the authors have applied several post-estimation tests to ensure the robustness of the results.

The results indicate that there is significant leverage effect in Islamic stocks traded in the sampled countries. That is, negative shocks or bad news have stronger effects on Islamic stock returns’ volatility as compared to positive shocks or good news. The authors also found that there are significant mean spillover effects for the examined countries. This finding implies that increased Islamic stock returns in country have significant and positive effects in Islamic stocks’ returns in another other. Similarly, the results regarding the volatility spillover effects suggest that there are significant volatility spillover effects across all examined countries. However, the authors found both positive and negative volatility spillover effects. It should also be noted that in some cases, the authors did not find any significant volatility spillover effect.

The findings of this study have several important policy implications for both investors and policymakers. As the findings suggest that Islamic stock indices are integrated across countries both in terms of returns (mean) and risk (volatility), they are useful for investors to design well-diversified portfolios. The significant volatility spillovers suggest policymakers to design such policy that may help in reducing the adverse effects of increased volatility of Islamic stock of other/foreign countries on the Islamic stocks of the home countries. The significant evidence of the presence of leverage (asymmetric) effects suggest investors to use effective and active hedging instruments to hedge risk, particularly, in bad times.

Unlike other studies on Islamic stocks, this study takes into account the asymmetric effects of positive and negative shocks. Further, the study examines the mean and variance spillover effects for a large panel of countries having Islamic stocks. Finally, several pre- and post-estimation tests are applied to ensure the robustness of the results.

The purpose of this paper is to study the distribution of active earth pressure in retaining walls with narrow cohesion less backfill considering arching effects.

To this end, the approach of principal stresses rotation was used to consider the arching effects.

According to the presented formulation, the active soil pressure distribution is nonlinear with zero value at the wall base. The proposed formulation implies that by increasing the frictional forces at both sides of the backfill, the arching effect is increased and so, the lateral earth pressure on the retaining wall is decreased. Also, by narrowing the backfill space, the lateral earth pressure is extremely decreased.

A comprehensive analytical solution for the active earth pressure of narrow backfills is presented, such that the effects of the surcharge and the characteristics of the stable back surface are considered. The magnitude and height of the application of lateral active force are also derived.

The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types.

Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.

Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types.

The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.

To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

This paper attempts to examine a number of issues regarding the returns on the Hang Seng Index Futures traded in Hong Kong. The daily returns are separated into close‐to‐close, close‐to‐open, and open‐to‐close periods and the three returns examined for autocorrelation, GARCH and seasonal effects. The study reveals that the CLCL returns are autocorrelated and that most of the returns exhibit GARCH effects. With regard to seasonal effects the results are mixed.

The purpose of this paper is to examine the existence of the day-of-the-week effect in the Indian stock market.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1), Exponential GARCH (EGARCH) (1, 1) and Threshold GARCH (TGARCH) (1, 1) models are employed to examine the day-of-the-week effect in the Indian stock market for the period of 28 years from 3rd July, 1990 to 31st March, 2022.

The empirical results derived from the GARCH models indicate the existence of day-of-the-week effects on stock returns and volatility of the Indian stock market. The study reveals that all the days of the week are positive and significant in National Stock Exchange (NSE)-Nifty market returns. The findings confirm the persistence of ARCH and GARCH effects in the daily return series. Moreover, the asymmetric GARCH models show that the daily stock returns exhibit significant asymmetric (leverage) effects.

The results of this study established that the Indian stock market is not efficient and there exists an opportunity to the traders for predicting the future prices and earning abnormal profits in the Indian stock market. The findings of the study are important for traders, investors and portfolio managers to earn abnormal returns by cross-border diversification.

First, to the best of the authors' knowledge, this paper is the first to study the day-of-the-week effect in Indian stock market considering the most recent and longer time period (1990–2022). Second, unlike previous research, this study used GARCH models (GARCH, EGARCH and TGARCH) to capture the volatility clustering in the data.

The purpose of this paper is to examine the effect of GST announcements (pre and post) on Malaysian stock market index. This study also utilised intraday data to look into intraday market volatility post-GST announcement.

Both daily closing prices and intraday data of different frequencies are used to capture the extent of stock market volatility as well as the subsided period of the volatility. The period of study ranges from June 2009 to November 2016 and empirical estimation is based on the GARCH (1, 1) model for the pre- and post-GST announcements.

Persistent market volatility in the post-GST announcement is empirically recorded and the volatility is higher in the post-GST announcement than the pre-GST announcement. This demonstrates the unwillingness and reaction of the market towards the tax policy implementation. Market expectation on GST implementation towards the increase in the cost of living following the increase in the prices of goods and services in Malaysia is empirically supported in the post-GST announcement.

The finding on this study is consistent with the expectation of the market that GST implementation will increase the price of the goods and services and hence increase the cost of living. This is supported by a noticeable increase in the stock market volatility in the post-GST announcement. Although GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails as shown by the increase in the stock market volatility.

The effects of Asian and global financial crisis are the major focus of past studies on stock market volatility, whereas this study examines and highlights the effect of the GST announcement on stock market volatility and the use of intraday data to further examine the nature of the volatility.

The purpose of this paper is to examine the housing price volatility for eight capital cities in Australia over 1987‐2007. Specifically, the volatility of Australian housing and its determinants were investigated.

An exponential‐generalised autoregressive conditional heteoskedasticity (EGARCH) model was employed to analyse the volatility for eight capital cities in Australia. The Engle LM test was also utilised to examine the volatility clustering effects in these cities.

The volatility clustering effects (ARCH effects) were found in many Australian capital cities. The importance of estimating each individual city's EGARCH model was also demonstrated in which the determinants of housing volatility vary from a city to another city. Asymmetric of the positive and negative shocks were also documented.

This study has implications for investors and policy makers in which housing investors should estimate the conditional variance (EGARCH process) of a housing market in respect to the volatility of housing series is not always constant over time. Furthermore, policy makers should also address the importance of considering the sub‐national factors in formulating the national housing policy. The analysis and results are limited by the quality of the data.

This paper is one of the few studies in housing volatility. Additionally, it is probably the first attempt to assess the volatility spillover effects in the Australian housing market.