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The BEKK GARCH class of models presents a popular set of tools for applied analysis of dynamic conditional covariances. Within this class the analyst faces a range of model choices that trade off flexibility with parameter parsimony. In the most flexible unrestricted BEKK the parameter dimensionality increases quickly with the number of variables. Covariance targeting decreases model dimensionality but induces a set of nonlinear constraints on the underlying parameter space that are difficult to implement. Recently, the rotated BEKK (RBEKK) has been proposed whereby a targeted BEKK model is applied after the spectral decomposition of the conditional covariance matrix. An easily estimable RBEKK implies a full albeit constrained BEKK for the unrotated returns. However, the degree of the implied restrictiveness is currently unknown. In this paper, we suggest a Bayesian approach to estimation of the BEKK model with targeting based on Constrained Hamiltonian Monte Carlo (CHMC). We take advantage of suitable parallelization of the problem within CHMC utilizing the newly available computing power of multi-core CPUs and Graphical Processing Units (GPUs) that enables us to deal effectively with the inherent nonlinear constraints posed by covariance targeting in relatively high dimensions. Using parallel CHMC we perform a model comparison in terms of predictive ability of the targeted BEKK with the RBEKK in the context of an application concerning a multivariate dynamic volatility analysis of a Dow Jones Industrial returns portfolio. Although the RBEKK does improve over a diagonal BEKK restriction, it is clearly dominated by the full targeted BEKK model.

Corporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market.

The static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH).

The study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model.

The findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market.

Majorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.

The aim of this study was to investigate the dynamic network connectedness between stock markets and commodity futures and its implications on hedging strategies. Specifically, the authors studied the impact of the 2014 oil price drop and coronavirus disease 2019 (COVID-19) pandemic on risk spillovers and portfolio allocation among stock markets (United States (SP500), China (SSEC), Japan (Nikkei 225), France (CAC40) and Germany (DAX)) and commodities (oil and gold).

In this study, the authors used the Baba, Engle, Kraft and Kroner–generalized autoregressive conditional heteroskedasticity (BEKK–GARCH) model to estimate shock transmission among the five financial markets and the two commodities. The authors rely on Diebold and Yılmaz (2014, 2015) methodology to construct network-associated measures.

Relying on the BEKK–GARCH, the authors found that the recent health crisis of COVID-19 intensified the volatility spillovers among stock markets and commodities. Using the dynamic network connectedness, the authors showed that at the 2014 oil price drop and the COVID-19 pandemic shock, the Nikkei225 moderated the transmission of volatility to the majority of markets. During the COVID-19 pandemic, the commodity markets are a net receiver of volatility shocks from stock markets. In addition, the SP500 stock market dominates the network connectedness dynamic during the COVID-19 pandemic, while DAX index is the weakest risk transmitter. Regarding the portfolio allocation and hedging strategies, the study showed that the oil market is the most vulnerable and risky as it was heavily affected by the two crises. The results show that gold is a hedging tool during turmoil periods.

This study contributes to knowledge in this area by improving our understanding of the influence of fluctuations in oil prices on the dynamics of the volatility connection between stock markets and commodities during the COVID-19 pandemic shock. The study’s findings provide more implications regarding portfolio management and hedging strategies that could help investors optimize their portfolios.

This paper aims at looking into the transmission of shocks between bond and oil markets using a bivariate GARCH (BEKK and DCC) model. As lots of financial assets have been exchanged due to these index returns, it is essential for financial market participants to figure out the mechanism of volatility transmission through time and via these series for the purpose of taking optimal decisions of portfolio allocation. The outcomes drawn reveal an important volatility transmission between sovereign bond and oil indices, with great sensitivity during and after the subprime crisis period.

In this context, we propose our hypotheses. Indeed, our study aims to see whether the financial crisis has been responsible for the sharp drop in oil prices since October 2008. To this end, we suggest, in this paper, the empirical study of the shock transmission between the bond and oil markets, using BEK-GARCH and DCC models. To our knowledge, this is the first document using the BEKK-GARCH and the DCC models in studying the shock transmission between a sovereign bond and oil indices.

We have noticed that in the event of a disruption in the bond market, oil prices respond to these shocks in the short term. It has also been emphasized, however, that this relationship has exacerbated if the period has extended. This makes us conclude that the financial market situation affects the oil price only throughout the crisis period; and that this situation is causally significant only in the event of a severe crisis, such as those of subprime and sovereign debt.

The global financial system has been going through an acute crisis since mid-2007. This crisis, initially occurred only in the US real estate market, progressively affects the global financial system, and is now becoming a general economic crisis. The objective of this work is to analyze the effects of the current financial market disturbance on oil prices based on econometric models in order to promote the proper functioning of this study.

This study aims to investigate the existence of contagion between liquid and illiquid assets in the credit default swap (CDS) market around the recent financial crisis. The authors perform analyses based on vector autoregression model and the dynamic conditional correlation model. The estimation of vector autoregression models reveals that changes in liquid CDS (LCDS) spreads lead to changes in illiquid CDS spreads at least one week ahead during the financial crisis period, whereas the leading direction is reversed during the post-crisis period. Moreover, the results are robust after controlling for structural variables which are proven as determinants of CDS spreads and are empirically supported. This study interprets that information was incorporated first into the LCDSs because of the flight-to-liquidity during the recent crisis period but there is a default contagion effect by reflecting illiquidity-induced credit risk after the crisis. Finally, the dynamic conditional correlation analysis also confirms the main results.

This paper aims to investigate the return links and volatility transmission between five major equity markets of the Latin American region and the USA over the period 1993-2012.

The authors employ a multivariate vector autoregressive moving average – generalized autoregressive conditional heteroskedasticity (VAR-GARCH) methodology which allows for cross-market transmissions in both return and volatility. Moreover, we show how the obtained results can be used to design internationally diversified portfolios involving the Latin American assets and to analyze the effectiveness of hedging strategies.

The results point to the existence of substantial cross-market return and volatility spillovers and are thus crucial for international portfolio management in the Latin American region. However, the intensity of shock and volatility cross effects varies across the studied markets.

The optimal weights and hedging ratios that we compute from the observed return and volatility spillovers, suggest that adding the Latin American assets helps improve the risk-adjusted return of the internationally diversified portfolios as well as reduce their risk exposure. For policymakers and market authorities, an increase in the level of shock interactions and volatility transmission between the US and Latin American equity markets as well as among these Latin American markets implies that the stability of the financial system in one country can be deeply affected by the disturbances in another country.

The authors extend the previous works on Latin American emerging markets by examining the extent of shock and volatility transmission as well as portfolio design and management from the point of view of both the US (global) and Latin American investors.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

This study investigates possible shock transmission and volatility spillover effects among the exchange rate changes and international portfolio flows for United States vis-à-vis two fast-growing emerging country groups: the BRICS (Brazil, Russia, India, China and South Africa) and MINT (Mexico, Indonesia, Nigeria and Turkey).

Applying VAR-BEKK-GARCH model, the evidence indicates that exchange rate fluctuations have a negative impact on net equity flows in Brazil, Russia, India and Turkey; thus, supporting the view that exchange rate uncertainty is an important driver of equity home bias.

As for the comparison of the pre- and post-crisis period, the findings support the evidence that the post-crisis period witnessed a greater number of cases of significant shock and volatility spillovers among exchange rate uncertainty and portfolio flows.

Overall, the empirical results provide fresh insights and policy implications for domestic and international investors through investment activities, and for policymakers through maintaining economic and financial stability.

The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.

Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.

Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.

Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.