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Undoubtedly, financial markets are not only affected by common economic, financial and behavioral factors but also by other global risk shocks. In this paper, we investigate the relevance of global geopolitical risk in stock-bond relationships. Specifically, we assess the possibility and manner in which time-varying correlations of Islamic stocks and bonds (sukuk) returns are affected by geopolitical risk and whether they behave differently from their conventional counterparts. We do so by implementing a multivariate GARCH model under dynamic conditional correlations (DCCs) to daily data of the Gulf Cooperation Council (GCC) markets (2013-2019). Our outcomes unveil that geopolitical risks positively affect the dependence between Islamic stocks and sukuk, as well as their conventional counterparts. Regional rather than global geopolitical risk factors positively affect the volatility of conventional stock-bond correlation, while the volatility of Islamic stock-sukuk correlation decreases during times of high geopolitical risks. Our findings offer several prominent implications in terms of financial stability, asset allocation of portfolios and policy design. [ABSTRACT FROM AUTHOR]

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises. [ABSTRACT FROM AUTHOR]

We use a Markov regime switching structural GARCH-in-Mean VAR model for the inflation rate, the output gap, and a short-term nominal interest rate, to investigate the relationship between inflation uncertainty and economic activity in the USA. We find that inflation uncertainty has a negative and statistically significant effect on the output gap. Also, inflation shocks have a negative effect on the output gap, irrespective of whether they are positive or negative, with the asymmetry being caused by the negative effects of inflation uncertainty. [ABSTRACT FROM AUTHOR]

Research on individual variation has received increased attention. The bulk of the models discussed in psychological research so far, focus mainly on the temporal development of the mean structure. We expand the view on within-person residual variability and present a new model parameterization derived from classic multivariate GARCH models used to predict and forecast volatility in financial time-series. We propose a new pdBEKK and a modified dynamic conditional correlation (DCC) model that accommodate external time-varying predictors for the within-person variance. The main goal of this work is to evaluate the potential usefulness of MGARCH models for research in within-person variability. MGARCH models partition the within-person variance into, at least, 3 components: An overall constant and unconditional baseline variance, a process that introduces variance conditional on previous innovations, or random shocks, and a process that governs the carry-over effects of previous conditional variance, similar to an AR model. These models allow for variance spillover effects from one time-series to another. We illustrate the pdBEKK- and the DCC-MGARCH on two individuals who have rated their daily positive and negative affect over 100 consecutive days. The full models comprised a multivariate ARMA(1,1) model for the means and included physical activity as moderator of the overall baseline variance. Overall, the pdBEKK seems to result in a more straightforward psychological interpretation, but the DCC is generally easier to estimate and can accommodate more simultaneous time-series. Both models require rather large amounts of datapoints to detect nonzero parameters. We provide an R-package bmgarch that facilitates the estimation of these types of models. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence. [ABSTRACT FROM AUTHOR]

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model. Design/methodology/approach: Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return. Findings: Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles. Originality/value: The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin's volatility on financial system stability. [ABSTRACT FROM AUTHOR]

This paper proposes a new parsimonious multivariate GARCH–jump (MGARCH–jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns and beta dynamics of a stock. Applied to daily stock returns, the model identifies co‐jumps well and shows that both jump arrivals and jump sizes are highly correlated. The jump model has better out‐of‐sample forecasts compared with a benchmark multivariate GARCH model. [ABSTRACT FROM AUTHOR]

The recent growth and development of aquaculture industry in Bangladesh demand a thorough investigation of price volatility spillover pattern among capture fisheries and aquaculture products. Utilizing nationally representative monthly retail price data of the last seventeen years, we identify the evidence of volatility spillover effects among capture fisheries and aquaculture markets. We find three phases of fish price movements: (1) fish prices, in general, were increasing prior to 2013 with higher fluctuations and structural breaks; (2) prices were comparatively stable from 2013 to 2019 for almost all markets with sporadic shocks; (3) increasing trajectory of fish prices are observed during the COVID-19 period. We also find that all the aquaculture products have significant direct or indirect long-run volatility spillover effect to capture fish market. We identify only one aquaculture product with a significant positive own price asymmetric effect indicating negative news increases the price volatility behavior in its own market. Our findings may play a vital role in implementing suitable food security policies and reducing overexploitation of natural resources. Policies promoting fish price stability should prioritize tilapia and pangasius markets, and targeted investments in the aquaculture industry should be made to promote greater stability in the fisheries and aquaculture market. [ABSTRACT FROM AUTHOR]

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test.

Interconnected economies in the world commodity market may cause external shocks that slow indigenous countries' economic growth and development. Nigeria is a commodity-dependent country, hence, commodity price shocks have harmed its institutional and fiscal framework for economic growth, despite efforts to save the economy. It is crucial to assess the effects of local and external commodity price volatility shocks on emerging nations' growth since they plunge millions into abject poverty. It is in this context, this study examines commodity price shock and economic performance with an empirical evidence for Nigeria. This study employs Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) to capture both gradual and unexpected changes in commodity price shock in Nigeria from 1980Q1 – 2021Q1. The results showed positive volatility shock effect on each variable caused by its past innovations while parameters of the independent variables indicated innovations of shock effects on economic performance. Also exports of other non-oil commodities can reduce dependence of real exchange rate on world commodity prices by having more diversified exports. The study recommends for diversification towards agricultural production to stabilize the real exchange rate of Naira from world commodity price shocks. Finally, Nigeria can protect her competitiveness from fluctuations in world commodity prices by being more opened to external trade. [ABSTRACT FROM AUTHOR]

This paper presents a term structure model for no-arbitrage bond yields and realized bond market volatility. Based on well-known results, realized yield curve covariation is linked to generalized autoregressive conditional heteroskedasticity (GARCH)-type conditional covariation. The model is tractable and its latent state variables can be filtered using an exact algorithm. In an empirical study of U.S. Treasury bond data, the model shows that conditional yield curve covariation is priced in long-term yields. Moreover, the model proves useful for multi-step ahead forecasting of realized covariation. Finally, I use the model to quantify interest-rate risk and risk compensation. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the spillover effects between energy markets and stock markets with emphasis on the significant crisis periods of the last 15 years, the period of the financial crisis that officially started in 2008, the pandemic period, generically called COVID-19, and the recent confrontation in Eastern Europe. Understanding the volatility transmission mechanisms between the energy and capital markets and also from the energy markets back and the spillover effects that result is very important. We use multivariate GARCH models to highlight a spillover effect between energy commodities and equities in Central and Eastern Europe. The highest correlations are recorded for CEE stock markets with electricity and Brent, and the lowest for CEE stock markets with gas. The biggest symmetric shocks between energy and CEE stock markets occurred during the COVID-19 pandemic. In contrast, the biggest asymmetric shocks occurred during the financial crisis (for gas) and the Ukrainian invasion (for Brent). We also find that volatility is more sensitive to its lagged values in the marketplace than it is to new information. The impact and contagion of shocks caused by the oil market are greater than those of other energy markets. [ABSTRACT FROM AUTHOR]

The growing number of cross‐listed stocks has significantly contributed to the integration of national economies over the last few decades, and has consequently strengthened the interdependence between international stock markets. This phenomenon has sparked a fervent debate in academia and in decision‐making circles dealing with volatility spillovers between international stock prices. Accordingly, isolated examinations of single foreign cross‐listing markets appear inadequate. On this basis, the aim of this article is to recognize volatility transmission patterns within international equity markets using a unique and comprehensive sample of highly traded companies simultaneously cross‐listed in the United States and the UK. An important fact to be noted here is that long‐range dependence is statistically proved in the studied cases, which means that the efficient market hypothesis is utterly rejected. Therefore, a multivariate fractionally co‐integrated model is preliminarily used to fit various samples of high‐frequency time series. Resulting residuals are then subject to a deeper analysis, allowing us to reveal significant volatility transmissions between markets of multiple cross‐listed stocks. Causality experiments are finally conducted on processed data. The results show strong bi‐directional volatility causations between various marketplaces. The overall deduction of this work is that price discovery takes place essentially in local and British markets, thus implying a satellite role in the price discovery process for American exchanges. [ABSTRACT FROM AUTHOR]

Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ( h t ). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test. [ABSTRACT FROM AUTHOR]

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors. [ABSTRACT FROM AUTHOR]

A model for dynamic independent component analysis is introduced where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. While conditional second moments are invariant with respect to rotations, higher conditional moments are not, which may have important implications for applications. The pseudo maximum likelihood estimator of the model is shown to be consistent and asymptotically normally distributed. A simulation study reports good finite sample properties of the estimator, including the case of a misspecification of the innovation density. In an application to a bivariate exchange rate series of the Euro and the British Pound against the U.S. Dollar, it is shown that the model-implied conditional portfolio kurtosis largely aligns with narratives on financial stress as a result of the global financial crisis in 2008, the European sovereign debt crisis (2010–2013) and early rumors signalling the United Kingdom to leave the European Union (2017). These insights are consistent with a recently proposed model that associates portfolio kurtosis with a geopolitical risk factor. [ABSTRACT FROM AUTHOR]

Cryptocurrencies and tourism have gained traction worldwide in the last few years. However, no research has been conducted to understand the relationship between the two. This paper examines the impact of the volatility spillover effects (VSE) of cryptocurrencies on the tourism sector in India. Using monthly time-series data (from August 2015 to January 2021) of the selected cryptocurrencies and foreign tourist arrivals (FTA) and foreign exchange earnings (FEE) from foreign tourism, we assess the volatility (short and long-term) impacts of cryptocurrencies on tourism (through changes in the monthly number of FTAs in India and FEEs of India through foreign tourism). The study applies Multivariate GARCH models (BEKK-GARCH and mGJR-GARCH). The findings suggest that there is an existence of volatility connections between cryptocurrencies and foreign tourism in India. These findings have noticeable implications for policymakers to understand the importance of cryptocurrency and blockchain for tourism sector policies in India. [ABSTRACT FROM AUTHOR]

The complexity of estimating multivariate GARCH models increases significantly with the increase in the number of asset series. To address this issue, we propose a general regularization framework for high-dimensional GARCH models with BEKK representations, and obtain a penalized quasi-maximum likelihood (PQML) estimator. Under some regularity conditions, we establish some theoretical properties, such as the sparsity and the consistency, of the PQML estimator for the BEKK representations. We then carry out simulation studies to show the performance of the proposed inference framework and the procedure for selecting tuning parameters. In addition, we apply the proposed framework to analyze volatility spillover and portfolio optimization problems, using daily prices of 18 U.S. stocks from January 2016 to January 2018, and show that the proposed framework outperforms some benchmark models.

Analysis of Dynamic Relationships between Financial assets is important in risk management and investment portfolio strategies. Investors try to diversify their assets in different markets in order to cover risk or optimize, and in this regard, they pay attention to the interaction between markets. In this research, the analysis of dynamic conditional correlation and Spillover of Volatility in the return of four cryptocurrencies including Bitcoin, Ethereum, Ripple and Litecoin from 16 Aug 2015 to 14 Jul 2022 has been done. The purpose of this research is to understand and identify the Volatility spillovers between the cryptocurrency market Coins and also to estimate the variable correlation over time between this category of assets with the pairwise dynamic conditional multivariate GARCH model. The results of this research show the existence of the two-way Spillovers effects among all investigated cryptocurrencies. Also, due to the fact that during the crisis, the correlation between the examined cryptocurrencies has increased significantly, evidence of the existence of asymmetric effects between the investigated cryptocurrencies can be confirmed. [ABSTRACT FROM AUTHOR]

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature. [ABSTRACT FROM AUTHOR]

• We investigate the asymmetric volatility spillover between oil and GCC stock markets. • The daily infectious disease equity market volatility index as a proxy for COVID-19 crisis. • A vector error correction asymmetric BEKK model implemented. • GCC stock markets seem to be less affected by the emergence of the new infectious disease. • Diversification opportunities offered by Gulf equity markets to international investors. This study investigates how the COVID-19 outbreak has shaped the volatility spillover between oil and Gulf Cooperation Council (GCC) stock markets. Contagion analysis is conducted by implementing a vector error correction (VECM) asymmetric BEKK model, wherein both cointegration and asymmetric features are considered. Financial market uncertainty caused by the recent health crisis is captured using Baker et al.'s (2020) newly developed infectious disease tracker. Our results indicate a significant discrepancy in the GCC group, as shock and volatility linkages between oil and equities are more apparent for some countries but not for others. The estimated VECM-asymmetric BEKK model reveals cross-market asymmetric spillover effects only in Kuwait, Qatar, and Saudi Arabia. We report that the global pandemic has strongly affected crude oil market volatility, while the GCC region seems to be less affected by the emergence of the new infectious disease. Our findings underscore the diversification opportunities offered by Gulf equity markets to international investors. [ABSTRACT FROM AUTHOR]

We first employ the method of multivariate GARCH models and Vine-Copula-CoVaR to analyse relationships between dependence, systematic risk spillover, and volatility spillover between the USD/CNY exchange rate and the returns on WTI crude oil futures and the Chinese stock market since China's 2005 foreign exchange reform. We utilise daily data from 2005 to 2020. We find a more complex dependence of the USD/CNY exchange rate on stock markets and WTI crude oil prices. All have negative risk spillovers among paired markets, with WTI having the most substantial risk spillover. However, the strength of the systematic risk spillover varies across markets. Based on the results of the VAR(1)-BEKK-GARCH (1,1) and Wald tests confirm that there is a substantial mean spillover from the Chinese stock market and the USD/CNY exchange rate to the WTI crude oil price, whereas there is a more significant spillover from the WTI crude oil price to Chinese stock market volatility. The empirical findings extend the systematic understanding of the international crude oil price shocks to the dependence and transmission mechanism between the Chinese stock market and the USD/CNY exchange rate (USD/CNY). Our findings can help investors and policymakers to manage risk better and develop more sensible market rules.

The objective of this paper is to study the spillover effects between energy markets and stock markets with emphasis on the significant crisis periods of the last 15 years, the period of the financial crisis that officially started in 2008, the pandemic period, generically called COVID-19, and the recent confrontation in Eastern Europe. Understanding the volatility transmission mechanisms between the energy and capital markets and also from the energy markets back and the spillover effects that result is very important. We use multivariate GARCH models to highlight a spillover effect between energy commodities and equities in Central and Eastern Europe. The highest correlations are recorded for CEE stock markets with electricity and Brent, and the lowest for CEE stock markets with gas. The biggest symmetric shocks between energy and CEE stock markets occurred during the COVID-19 pandemic. In contrast, the biggest asymmetric shocks occurred during the financial crisis (for gas) and the Ukrainian invasion (for Brent). We also find that volatility is more sensitive to its lagged values in the marketplace than it is to new information. The impact and contagion of shocks caused by the oil market are greater than those of other energy markets.

We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out‐of‐target‐portfolio information that may be missed when one considers the value at risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of dimensionality can be overcome in the use of financial big data and discuss where and when benefits occur from a large number of assets. In this regard, the proposed approach is the first to suggest the use of financial big data to improve the accuracy of risk analysis. We compare the proposed model with benchmark approaches and empirically show that the use of financial big data improves small portfolio risk analysis. Our findings are useful for portfolio managers and financial regulators, who may seek for an innovation to improve the accuracy of portfolio risk estimation. [ABSTRACT FROM AUTHOR]

The hypothesis of excess co-movement in commodity markets indicates simultaneous price movements, which led to different consequences on the supply and demand side. These consequences include diverting resources from productive activities on the supply side and creating widespread inflationary pressures on the demand side. In the present study, the co-movement of the different types of animal meat was assessed over time. In addition, the other purpose of this study is to determine the impact of the factors affecting co-movement. For this purpose, first, by using the ARMA-DCC-GARCH approach, Co-movement among the meat types market in Iran was estimated. Then, using Quantile regression analysis, the effect of the most critical factors affecting conditional co-movement was estimated. The results showed that the co-movement among the meat types market has experienced significant changes over time and has been decreasing in some periods and increasing in others. The Maximum and minimum value of the dynamic conditional correlations were -0.58 and -0.08 between fish and poultry meat markets, 0.44 and 0.02 between fish and red meat markets, and 0.46 and -0.04 between chicken and red meat markets, respectively. In addition, the most critical determinants of Co-movement between meat types market include exchange rates, exchange rate volatility, production costs, liquidity, energy market fluctuations, and volatile monetary policymaking. Therefore, it is recommended that policymakers, given the role of these factors in their decisions, prevent the occurrence of periods with high co-movement. [ABSTRACT FROM AUTHOR]

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the 'curse of dimensionality'. Recently, Laurent, Rombouts and Violante (2014 "Multivariate Rotated ARCH Models" Journal of Econometrics179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a method for modelling volatilities (conditional covariance matrices) of high dimensional dynamic data. We combine the ideas of approximate factor models for dimension reduction and multivariate GARCH models to establish a model to describe the dynamics of high dimensional volatilities. Sparsity condition and thresholding technique are applied to the estimation of the error covariance matrices, and quasi maximum likelihood estimation (QMLE) method is used to estimate the parameters of the common factor conditional covariance matrix. Asymptotic theories are developed for the proposed estimation. Monte Carlo simulation studies and real data examples are presented to support the methodology. [ABSTRACT FROM AUTHOR]

Purpose – It is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP). Design/methodology/approach – This study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies. Findings – Dynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark. Practical implications – Sophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio. Originality/value – To the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.

We propose sparse DCC‐GARCH and BEKK‐GARCH models based on L1 ${L}_{1}$ regularization. We use the models to study daily return volatility and correlation spillovers for the 24 constituents of the Bloomberg commodity index in the period 2000–2018. The sparse models outperform the diagonal models out‐of‐sample in terms of model fit and other criteria. We also test whether the higher visibility of metals and energy markets compared with agricultural commodities affects the speed of information processing. We find correlation spillovers from metals and energy to agricultural commodities even though the latter tend to settle somewhat later. [ABSTRACT FROM AUTHOR]

The article examines exchange-traded funds (ETFs) for green and sustainable energy regarding causality in their asset returns and volatilities. The structural vector autoregressive (VAR) model is one of the popular methodologies for the empirical analysis of macroeconomics and finance. However, the analysis is limited to the conditional mean, and excludes the structural analysis of conditional covariance models which measure the volatility and co-volatility of the financial asset returns. In order to accommodate this limitation, we develop a new structural multivariate GARCH-BEKK model that accommodates a dimension reduction for a BEKK-type parameterization of the time-varying covariance structure. We use a quasi-maximum likelihood estimator, which is shown to have consistency and asymptotic normality. For energy ETF returns, we construct the structural GARCH-BEKK model in order to investigate the causality in returns and volatility via statistical tests and impulse response functions, especially for two events, namely a drop in crude oil prices on May 5, 2011, and the Fukushima nuclear disaster on March 11, 2011. Our empirical results have found that for the portfolio renewable energy ETFs, Solar, Wind, and Water seem to exhibit indirect mutual causality effects in mean, and direct mutual causality effects in the second moment. When we incorporate the oil market into the renewable energy market, Oil seems to dominate the causality effects, so indirect uni- causality effects of the mean from the Oil to the Solar ETF, or Oil to the Water ETF, are found. However, there are no uni-causality or mutual causality effects in the second moment. [ABSTRACT FROM AUTHOR]

This study uses daily return data from seven sector indexes, Ibovespa index and Dollar to analyze volatility spillovers through conditional correlations, aiming at identifying the behavior of the sectors. Univariate GARCH models demonstrate that the return series of the sectoral indexes are characterized by the presence of heavy tails, skewness and kurtosis and were used as an indication of the multivariate model orders. Diagonal Multivariate GARCH-BEKK models with skewness parameters and low orders represented satisfactorily the sectoral indices generating series. Conditional correlations were estimated for three different models, combining the volatility of each sector with different shocks: dollar volatility, Ibovespa volatility and dollar and Ibovespa volatility. CN tests were used to check for the presence of volatility spillovers and the directions, in order to characterize lead-lag effects. [ABSTRACT FROM AUTHOR]

This study develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. An alternative multivariate asymmetric function to develop news impact curves is also considered. We consider Bayesian Markov chain Monte Carlo estimation to allow non-normal posterior distributions and illustrate the usefulness of the algorithm with numerical simulations for two assets. We compare the realized MEGARCH models with existing multivariate GARCH class models for three US financial assets. The empirical results indicate that the realized MEGARCH models outperform the other models regarding out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results. [ABSTRACT FROM AUTHOR]

The aim of the paper is to examine the stock market integration between Western Europe and selected countries of Central (Austria, Czech Republic, Poland, Hungary, Slovakia, and Slovenia) and South-Eastern Europe (Greece, Croatia, Serbia, Bosnia, Bulgaria, and Romania). In order to achieve this goal, we used a bivariate BEKK model to obtain time-varying covariances and correlations for the period April 15, 2013 - March 29, 2019. Our results showed that Austria has the highest degree of integration among countries in Central Europe, followed by the Czech Republic, Poland and Hungary. Additionally, Greece has the highest degree of integration among all countries in South-Eastern Europe, followed by Romania, and Croatia. Thus, stock markets of Central Europe are more integrated with Western Europe than stock markets of South-Eastern Europe. [ABSTRACT FROM AUTHOR]

In this paper, in order to calculate portfolio market risk of 10 selected industries indices in Tehran Stock Exchange, two models of Value Risk (VaR) and Expected shortfall (ES) have been used. Different models of multivariate GARCH and various Coppola models have been used in order to estimate the volatility of the portfolio and nonlinear correlation of asset portfolio. Backtesting has been done by Kupiec, Christoffersen, Engle and Manganelli and McNeill and Ferry tests. Results show that the DCC-GARCH model by t-Student distribution compared to other competing models has the best results in estimating volatility of the asset portfolio. Also among all Copula models reviewed in this paper, t-student copula model has shown better results for estimating asset dependence. Finally, the results of backtesting of different models showed that both the DCC-GARCH model with t-Student distribution and DCC-GARCH-Copula with t-Student distribution have acceptable results in estimating VaR and ES. However, the Lopez and Blanco and Ihle tests showed that the DCC-GARCH model with t-Student distribution compared to the DCC-GARCH-Copula model with t-Student distribution gives a more accurate and efficient estimate of the VaR and ES of asset portfolios.