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Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

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.

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.

This study aims to test the mean and volatility spill over from the environmental, social, and governance (ESG) market to the stock markets of G7 countries. The study used ARMA-GARCH model to predict the results. The findings of the study reveal that as the spill over exists in the markets, however the mean volatility does not exist showing efficiency of the market as significant results depict that past prices cannot predict the future prices. It provides new insights for the international portfolio investors and policymakers by shedding light on how cross-markets correlate in two different markets.

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 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.

Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.

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 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.