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In the finance literature, fitting a cross-sectional regression with (estimated) abnormal returns as the dependent variable and firm-specific variables (e.g. financial ratios) as independent variables has become de rigueur for a publishable event study. In the absence of skewness and/or kurtosis the explanatory variable, the regression design does not exhibit leverage – an issue that has been addressed in the econometrics literature on the finite sample properties of heteroskedastic-consistent (HC) standard errors, but not in the finance literature on event studies. The paper aims to discuss this issue.

In this paper, simulations are designed to evaluate the potential bias in the standard error of the regression coefficient when the regression design includes “points of high leverage” (Chesher and Jewitt, 1987) and heteroskedasticity. The empirical distributions of test statistics are tabulated from ordinary least squares, weighted least squares, and HC standard errors.

None of the test statistics examined in these simulations are uniformly robust with regard to conditional heteroskedasticity when the regression includes “points of high leverage.” In some cases the bias can be quite large: an empirical rejection rate as high as 25 percent for a 5 percent nominal significance level. Further, the bias in OLS HC standard errors may be attenuated but not fully corrected with a “wild bootstrap.”

If the researcher suspects an event-induced increase in return variances, tests for conditional heteroskedasticity should be conducted and the regressor matrix should be evaluated for observations that exhibit a high degree of leverage.

This paper is a modest step toward filling a gap on the finite sample properties of HC standard errors in the event methodology literature.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.

Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.

This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well.

To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive (1) dynamic conditional correlation–asymmetric generalized autoregressive conditional heteroskedastic model in a multivariate framework and exponential generalized autoregressive conditional heteroskedastic model in mean with covariances as explanatory variables are used. For an in-depth analysis, Markov regime switching model and optimal hedging ratios and weights are also computed. The span of data ranges from August 10, 2010 to August 7, 2015, especially after the global financial crisis.

The Indian equity market is not completely decoupled from mature markets as well as emerging market (China), but the time-varying correlation coefficients are on a downward spree after the global financial crisis, except for the US market. The Indian and Chinese equity markets witness a highest level of correlation with each other, followed by the European, US and Japanese markets. Both the optimal portfolio hedge ratios and portfolio weights with two asset classes point out toward portfolio risk minimization through the combination of the Indian and US equity market stocks from a US investor viewpoint. A negative co-movement between the Indian and US market increases the conditional expected returns in the Indian equity market. There is an insignificant but a negative relationship between the expected risk and returns.

The study provides an insight to the international as well as domestic investors and supports the construction of cross-country portfolios and risk management especially after the occurrence of global financial crisis.

The present study contributes to the literature in three senses. First, the period relates to the events after the global financial crisis (2007-2009). Second, the study examines the co-movement of the Indian equity market with four major economic giants such as the USA, Europe, Japan and China in a multivariate framework. These economic giants are excessively following the easy money policies aftermath the financial crisis so as to wriggle out of deflationary phases. Finally, the study captures risk-return relationship in the Indian equity market, considering its covariance with the international markets.

The aim of this study is to measure portfolio diversification benefits of the Turkey-based equity investors into top trading partner countries. Portfolio diversification benefits are analyzed from the viewpoint of two types of investors in Turkey: conventional equities investors and Islamic equity investors.

In order to evaluate the time-varying correlations of the trading partner country's stock index returns with the Turkish stock index returns, the multivariate-generalized autoregressive conditional heteroskedasticity–dynamic conditional correlation (GARCH-DCC) is applied based on daily data covering 13 years' period between January 22, 2008 and January 22, 2021.

The results revealed that the US stock indices provide the most diversified benefit for both conventional and Islamic Turkey-based equity investors. In general, Islamic indices exhibit relatively lower correlation with trading partners than conventional indices. Turkey and Russia are recorded as the most volatile indices.

The diversification potential in trading partners for Turkey-based Islamic equity investors has not been studied yet. This study is to fill in this gap in the literature and to give fruitful insights to both conventional and Islamic investors.

The purpose of this paper is to reinvestigate the performance of common stock returns with respect to two popularly known firm level characteristics: size and book‐to‐market ratio.

All of New York Stock Exchange, American Stock Exchange, and National Association of Securities Dealers Automated Quotations stocks between July 1926 and June 2007 are used, and divided into various size and book‐to‐market equity groups. The extension of the various versions of the simple Fama‐French model is implemented.

From the findings, it is inferred that: two risk factors based on the mimicking return for the size and book‐to‐market ratio play a significant role in capturing strong variation in stock returns; and volatility persistence can significantly improve the common risk factors' impact in explaining the time series variation in size and book‐to‐market sorted portfolios.

In some sense, the model is based on only two firm level variables. In reality there exists plenty of other sources of average return anomalies. For a clearer understanding, an integration of various firm level characteristics would be an interesting issue to explore. A general equilibrium model that incorporates volatility exposure in a Fama‐French framework would be a challenging task as well.

The approach will help scholars and investment professionals make robust quantification of risk and average returns with respect to various measures of fundamental value.

The patterns in the monthly and yearly average excess returns with respect to two firm level characteristics, which documented are consistent with earlier studies. Even though the important role of firm level characteristics on the average‐return anomalies of common stocks is widely known, the approach is the very first that extends its support with respect to volatility models.

The purpose of this paper is to examine the volatility spillover and lead-lag relationship between the Chicago Board Options Exchange volatility index (VIX) and the major agricultural future markets before and during the Coronavirus disease 2019 (COVID-19) outbreak.

The methods used were the vector autoregression-Baba, Engle, Kraft and Kroner-generalized autoregressive conditional heteroskedasticity method, the Wald test and wavelet transform method.

The findings indicate that prior to the COVID-19 outbreak, there was a two-way volatility spillover impact between the majority of the sample markets. In comparison, volatility transmission between the VIX index and the agricultural future market was significantly lower following the COVID-19 outbreak, the authors observed greater coherence at higher frequencies than at lower frequencies, implying that the interdependence between the two VIX indices and the agricultural future market was stronger over a longer time-frequency domain and the VIX’s signalling effect on various agricultural future prices after the COVID-19 outbreak was significantly lower.

The authors conducted the first comprehensive investigation of the VIX’s correlation with major agricultural futures, especially during COVID-19. The findings contribute to a better understanding of the risk transmission mechanism between the VIX and major agricultural commodities futures contracts. And our findings have significant implications for investors and portfolio managers, as well as for policymakers who are concerned about the price of agricultural futures.

The purpose of this paper is to examine empirically the impact of COVID-19 pandemic news in USA and in China on the dynamic conditional correlation between Bitcoin and Gold.

This paper offers a crucial viewpoint to the predictive capacity of COVID-19 surprises and production pronouncements for the dynamic conditional correlation (DCC) among Bitcoin and Gold returns and volatilities using generalized autoregressive conditional heteroskedasticity-DCC-(1,1) through the period of study since July 1, 2019 to June 30, 2020. To assess the unexpected impact of COVID-19, this study pursues the Kuttner’s (2001) methodology.

The empirical findings indicate strong important correlation among Bitcoin and Gold if COVID-19 surprises are integrated in variance. This study validates the financialization hypothesis of Bitcoin and Gold. The correlation between Bitcoin and Gold begin to react significantly further in the case of COVID-19 surprises in USA than those in China.

This paper contributes to the literature on assessing the impact of COVID-19 confirmed cases surprises on the correlation between Bitcoin and Gold. This paper gives for the first time an approach to capture the COVID-19 surprise component. Also, this study helps to improve financial backers and policymakers' comprehension of the digital currencies' market elements, particularly in the hours of amazingly unpleasant and inconspicuous occasions.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.