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Reviews previous research based on event study methodology, pointing out that events can influence returns in many ways, and applies the method to a sample of mergers and acquisitions in the thinly traded Norwegian market 1983‐1994. Explains how the classic market model can be adjusted to control for non‐synchronous trading and changing/asymmetric volatility; and how the event and non‐event periods can be combined into a single model. Applies two different models to the data, compares the results and finds the ARMA‐GARCH approach superior to the OLS. Discusses the implications of this for researchers.

The purpose of this paper is to extend the literature on the relationship between inflation and inflation uncertainty by examining three Caribbean countries: the Bahamas, Barbados, and Jamaica.

ARMA‐GARCH models are used to estimate inflation uncertainty along with Granger‐causality tests to infer the relationship between inflation and inflation uncertainty.

The results reveal that both the Bahamas and Jamaica exhibit a high degree of volatility persistence in response to inflationary shocks, while Barbados has a much lower persistence measure. Granger‐causality tests indicate that an increase in inflation has been a positive impact on inflation uncertainty for each country. However, an increase in inflation uncertainty yields a decrease in inflation in the case of Jamaica. In summary, the results for the Bahamas and Barbados support the Friedman‐Ball hypothesis, whereas the results for Jamaica support Holland's stabilization‐motive hypothesis.

Future research on inflation and inflation uncertainty can be extended to incorporate possible regime shifts associated with fiscal and monetary policy.

The study fills a void in the literature with respect to the inflation‐inflation uncertainty nexus for Caribbean countries. The results of the paper may be useful to policymakers in the formulation of fiscal and monetary policy.

The purpose of this paper is to analyze the Brexit effect – pre-Brexit and post-Brexit referendum periods – on the co-movements between the British pound (GBP), the euro (EUR) and the yen (JPY) against the US dollar (USD).

To ascertain the asymmetric behavior of dynamic correlations, the authors use the dynamic conditional correlation (DCC) model, the asymmetric dynamic conditional correlation (A-DCC) model and the diagonal BEKK model assuming Gaussian and Student’s t distribution. Several dummy variables have been included in order to identify the main periods related to Brexit.

Findings show a negative impact of the pre-Brexit referendum period on the correlation between GBP and EUR, while there is no significant effect on GBP–JPY and EUR–JPY pairs. The loss of correlation in the GBP–EUR pairing has not recovered during the post-Brexit referendum period, which could be attributed to the uncertainty about the final impact of Brexit on British and Eurozone economies.

The loss of correlation in the GBP–EUR pair has important implications for individual investors, portfolio managers and traders with respect to hedging activities, international trading and investment strategies.

The results are the first to address how Brexit has impacted on the co-movements between exchange rates using different multivariate models that allow for correlations to change over time.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

In Islamic finance (IF), the safety-first rule of investing (hifdh al mal) is held to be of utmost importance. In view of the instability in the global financial markets, the IF portfolio manager (mudharib) is committed, according to Sharia, to make use of advanced models and reliable tools. This paper seeks to address these issues.

In this paper, the limitations of the standard models used in the IF industry are reviewed. Then, a framework was set forth for a reliable modeling of the IF markets, especially in extreme events and highly volatile periods. Based on the empirical evidence, the framework offers an improved tool to ameliorate the evaluation of Islamic stock market risk exposure and to reduce the costs of Islamic risk management.

Based on the empirical evidence, the framework offers an improved tool to ameliorate the evaluation of Islamic stock market risk exposure and to reduce the costs of Islamic risk management.

In IF, the portfolio manager – mudharib – according to Sharia, should ensure the adequacy of the mathematical and statistical tools used to model and control portfolio risk. This task became more complicated because of the increase in risk, as measured via market volatility, during the financial crisis that began in the summer of 2007. Sharia condemns the portfolio manager who demonstrates negligence and may hold him accountable for losses for failing to select the proper analytical tools. As Sharia guidelines hold the safety-first principle of investing rule (hifdh al mal) to be of utmost importance, the portfolio manager should avoid speculative investments and strategies that would lead to significant losses during periods of high market volatility.

It is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.

The key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.

The backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.

This paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

The stock market is vulnerable to various exogenous factors, and its fluctuations can reflect the effects of political, economic and market factors. The purpose of this paper is therefore to choose the stock market as a representative to analyze the potential impact of the Brexit event on global financial markets and how to prevent the spread of risks across global financial markets.

This study chooses the auto-regressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model to fit the financial series and uses it as the marginal distribution model to establish the vine copula model. The maximum spanning tree algorithm is used to select the optimal rattan structure model and pair-copula function. According to the final ARMA-GARCH-R-vine copula model, the tail correlation coefficients of the UK, France, Germany, USA and China stock markets are calculated and used to analyze their dependence structure.

The negative impact of the Brexit event on the British stock market is greater and is more likely to be transmitted to France and Germany. China and the USA are less likely to be impacted by the Brexit incident. The US financial market is more closely linked to France, and it may benefit from the Brexit incident due to the impact of the exchange rate. Although the Chinese stock market is directly connected to the British stock market, due to the existence of national macro-controls and other factors, it will be less affected by the Brexit incident. The main impact comes from the dual devaluation pressure on the RMB.

This paper selects the optimal combination model based on actual data, and the results obtained can accurately reflect the interdependence between relevant stock markets and can guide risk aversion in the financial investment field.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.

The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns.

The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016.

The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return.

The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same.

ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

The purpose of this paper is to focus on the influence of mood/emotions and religious experience on Islamic stock markets during the Ramadan month.

This study uses stock returns data of two countries – Saudi Arabia and Iran – from January 2008 to September 2014 and the ARMA-GARCH models to study impact of the Ramadan month on the return and volatility of the stock market in these two countries.

The results of this study show some differences in the impact of the Ramadan month on the return and volatility of the stock market in these two countries. While the Ramadan month has a significant positive influence on the mean returns and the volatility of the Saudi market, its influence on the Iranian market is found to be insignificant. Further analysis on the last ten days of the Ramadan month provides a similar result for the Saudi market. However, for the Iranian market, volatility is significantly negatively affected during these last ten days.

Most prior studies have found significant changes in returns during the Ramadan month but a deeper understanding of this stock market anomaly is needed. The results point toward the influence of mood/emotions and religious experience in explaining the existence of the Ramadan anomaly.