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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.

The phenomenon of positive autocorrelation in daily stock index returns is often viewed as a consequence of stable behavioural patterns of certain investor groups (see, e.g., Sentana & Wadhwani, 1992; Koutmos, 1997). However, such patterns may change due to extreme events, that is, financial crises, and thus affect the autocorrelation in returns. Emerging markets and especially BRIC countries have experienced severe crises in the last 20 years and are therefore a suitable object for studying this effect.

The focus of this chapter is on identifying substantial changes in the autocorrelation of BRIC markets' index returns after experiencing upheavals of the financial system. For this purpose, we look for structural breaks in the parameters of an ARMA–GARCH model with the standard endogenous search procedure.

Our approach yields no statistically significant evidence of the autocorrelation changes due to the crises. Only in India the decline in autocorrelation in 1998 seems to be economically relevant, but is not significant statistically. Significant shifts that we could identify were rather related to microstructural changes, such as abolishment of price change limits by China and the removal of a leading player in India's market in 1992. All in all our results suggest that even though extreme negative events on financial markets may induce changes in feedback trading strategies, their influence on autocorrelation is not pronounced enough. The impact of other factors, in the first place of regulatory changes, seems to be of larger relevance.

Value at risk (VaR) is a market risk measure widely used by risk managers and market regulatory authorities, and various methods are proposed in the literature for its estimation. However, limited studies discuss its distribution or its confidence intervals. The purpose of this paper is to compare different techniques for computing such intervals to identify the scenarios under which such confidence interval techniques perform properly.

The methods that are included in the comparison are based on asymptotic normality, extreme value theory and subsample bootstrap. The evaluation is done by computing the coverage rates for each method through Monte Carlo simulations under certain scenarios. The scenarios consider different persistence degrees in mean and variance, sample sizes, VaR probability levels, confidence levels of the intervals and distributions of the standardized errors. Additionally, an empirical application for the stock market index returns of G7 countries is presented.

The simulation exercises show that the methods that were considered in the study are only valid for high quantiles. In particular, in terms of coverage rates, there is a good performance for VaR(99 per cent) and bad performance for VaR(95 per cent) and VaR(90 per cent). The results are confirmed by an empirical application for the stock market index returns of G7 countries.

The findings of the study suggest that the methods that were considered to estimate VaR confidence interval are appropriated when considering high quantiles such as VaR(99 per cent). However, using these methods for smaller quantiles, such as VaR(95 per cent) and VaR(90 per cent), is not recommended.

This study is the first one, as far as it is known, to identify the scenarios under which the methods for estimating the VaR confidence intervals perform properly. The findings are supported by simulation and empirical exercises.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real estate portfolio returns. The purpose of the paper is to estimate accurate ten-day-ahead 99%VaR forecasts for real estate markets along with stock markets for seven countries across the world (the USA, the UK, Germany, Japan, Australia, Hong Kong and Singapore) following the Basel Committee requirements for financial regulation.

A 14-dimensional multivariate Diag-VECH model for seven equity indices and their relative real estate indices is estimated. The authors evaluate the VaR forecasts over a period of two weeks in calendar time, or ten-trading-days, and at 99 percent confidence level based on the Basle Committee on Banking Supervision requirements.

The Basel regulations require ten-day-ahead 99%VaR forecasts. This is the first study that provides successful evidence for ten-day-ahead 99%VaR estimations for real estate markets. Additionally, the authors provide evidence that there is a statistically significant relationship between the magnitude of the ten-day-ahead 99%VaR and the level of dynamic correlation for real estate and stock market indices; a valuable recommendation for risk managers who forecast risk across markets.

Risk managers, investors and financial institutions require dynamic multi-period VaR forecasts that will take into account properties of financial time series. Such accurate dynamic forecasts lead to successful decisions for controlling market risks.

This paper is the first approach which models simultaneously the volatility and VaR estimates for real estate and stock markets from the USA, Europe and Asia-Pacific over a period of more than 20 years. Additionally, the local correlation between stock and real estate indices has statistically significant explanatory power in estimating the ten-day-ahead 99%VaR.

This paper aims to explore the empirical determinants of exchange-rate volatility (ERV) in selected Asian economies, namely, Bangladesh, China, India, Indonesia, Malaysia and Pakistan. Specifically, it examines how the volatility of foreign reserves, government spending, industrial production, gold prices and terms of trade affect monthly ERV during the examined period.

The authors carry out the empirical analysis by using monthly data for the period January 1997–March 2019. First, the volatility of the underlying variables is measured based on the conditional variances obtained by estimating the univariate (generalized) autoregressive conditional heteroskedasticity [(G)ARCH] model for each variable during the study period. Next, the autoregressive conditional heteroscedasticity (ARCH)-Lagrange multiplier test is applied to ensure that there are no remaining ARCH effects in the residuals. Finally, the multivariate autoregressive-moving average-GARCH (1, 1) models are estimated to examine whether and how the volatility of the underlying variables affects ERV.

The results reveal that the current period volatility of exchange rates is significantly affected by ERV in the previous period in all selected countries. The results also indicate that the volatilities of the underlying macroeconomic variables are quite differently related to ERV in examined Asian countries. Foreign-reserve volatility (VFXRES) has negative and significant impacts on ERV in Bangladesh, China and Malaysia. Government-spending volatility is negatively related to ERV in India, whereas it is positively related to ERV in all other examined countries. The results also suggest that although terms-of-trade volatility reduces ERV in both Bangladesh and Pakistan, it amplifies ERV in the remaining examined countries. However, gold-price volatility (VGOLDP) significantly, positively contributes to ERV in Bangladesh, Indonesia and Malaysia. On the contrary, the higher volatility in industrial production (VIPI) results in lower ERV in Indonesia and Pakistan, whereas it increases ERV in China, India and Malaysia.

The findings have several important policy implications. First, the findings suggest that both Bangladesh and Malaysia should keep an adequate level of foreign reserves to stabilize their foreign exchange rates. Second, as government-spending volatility has a vital role in determining ERV, it is necessary to bring sustainability and continuity in government expenditures. Bangladesh and Pakistan can stabilize their foreign exchange rates by making exports more competitive, viable and accessible.

This paper significantly contributes to the existing literature by exploring how the behavior of unexpected variations in the factors determining exchange rates affects ERV in selected Asia countries. Most of the published studies have examined the determinants of exchange rates by considering the macroeconomic variables at their levels. Departing from the existing studies, this paper significantly relates the volatility (second moment) of exchange rate determinants to the behavior of ERV. Further, this paper provides firsthand empirical evidence on this issue for the selected Asian economies.

The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of foreign currencies during the Covid-19 pandemic.

The authors consider a multivariate time series model where marginal dynamics are driven by an autoregressive moving average (ARMA)–Glosten-Jagannathan-Runkle–generalized autoregressive conditional heteroscedastic (GARCH) model, and the dependence structure among the residuals is given by an elliptical copula function. The correlation coefficient follows an autoregressive equation where the autoregressive coefficient is a function of the past values of the correlation. The model is applied to a portfolio of a couple of exchange rates, specifically US dollar–Japanese Yen and US dollar–Euro and compared with two alternative specifications of the correlation coefficient: constant and with autoregressive dynamics.

The use of the new model results in a more conservative evaluation of the tail risk of the portfolio measured by the value-at-risk and the expected shortfall suggesting a more prudential capital allocation policy.

The main contribution of the paper consists in the introduction of a time-varying correlation model where the past values of the correlation coefficient impact on the autoregressive structure.