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Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present article marries these two strands of literature to deliver improved estimation of large dynamic covariance matrices. Supplementary material for this article is available online.

This study described an empirical link between COVID-19 fear and stock market volatility. Studying COVID-19 fear with stock market volatility is crucial for planning adequate portfolio diversification in international financial markets. The study used AR (1) - GARCH (1,1) to measure stock market volatility associated with the COVID-19 pandemic. Our findings suggest that COVID-19 fear is the ultimate cause driving public attention and stock market volatility. The results demonstrate that stock market performance and GDP growth decreased significantly through average increases during the pandemic. Further, with a 1% increase in COVID-19 cases, the stock return and GDP decreased by 0.8%, 0.56%, respectively. However, GDP growth demonstrated a slight movement with stock exchange. Moreover, public attention to the attitude of buying or selling was highly dependent on the COVID-19 pandemic reported cases index, death index, and global fear index. Consequently, investment in the gold market, rather than in the stock market, is recommended. The study also suggests policy implications for key stakeholders.

This paper presents a novel wavelet-based quantile-on-quantile method for comparing the impact of COVID-19 on stock market volatility between the U.S. and China. Wavelet decomposition shows that the impact has stronger regularity in the lower frequency domain. Compared with oil price fluctuations, COVID-19 is the main reason for the sharp fluctuation of the U.S. stock market. Unlike China, however, the strong growth of daily new cases, which continued for months, has made the U.S. stock market insensitive to COVID-19. In addition, the particularly loose interest rate policy has effectively suppressed the volatility of the U.S. stock market. However, in contrast to China, the near zero interest rate applied by the U.S. makes it difficult to generate sufficient monetary policy space to address a new potential crisis. The result of this study presents the differences of the financial market response under different epidemic management modes. Under the background that COVID-19 is not effectively controlled, a loose monetary policy may be an expedient measure to stabilise the market. This is of great practical significance towards achieving epidemic control and financial market stability under the background of the global spread of COVID-19.

This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.

We introduce the realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.

I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of the squared innovations implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions on the variance process. In contrast, previous approaches exploiting heteroskedasticity rely on the path of innovation covariances, which can only be recovered from the data under specific parametric assumptions on the variance process. The conditions for identification are testable. I compare the identification scheme to existing approaches in simulations and provide guidance for estimation and inference. I use the methodology to estimate fiscal multipliers peaking at 0.86 for tax cuts and 0.75 for government spending. I find that tax shocks explain more variation in output at longer horizons. The empirical implications of my estimates are more consistent with theory and the narrative record than those based on some leading approaches.

This article addresses some of the issues that arise with the Dynamic Conditional Correlation (DCC) model. It is proven that the DCC large system estimator can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can result in misleading conclusions. Here, we suggest a more tractable DCC model, called the cDCC model. The cDCC model allows for a large system estimator that is heuristically proven to be consistent. Sufficient stationarity conditions for cDCC processes of interest are established. The empirical performances of the DCC and cDCC large system estimators are compared via simulations and applications to real data.

This paper investigates the effect of oil price uncertainty on global real economic activity using a quarterly vector autoregressive model with stochastic volatility in mean. Stochastic volatility allows oil price uncertainty to vary separately from changes in the level of oil prices, and allows one to incorporate an extraneous indicator of oil price uncertainty such as realized volatility that greatly improves the precision of the estimated uncertainty series. The estimation results show that an oil price uncertainty shock has negative effects on world industrial production all else equal. For example, it is shown that a doubling of oil price volatility is associated with a cumulative decline as high as 0.3 percentage points in world industrial production.

We introduce a new framework, Realized GARCH, for the joint modeling of returns and realized measures of volatility. A key feature is a measurement equation that relates the realized measure to the conditional variance of returns. The measurement equation facilitates a simple modeling of the dependence between returns and future volatility. Realized GARCH models with a linear or log-linear specification have many attractive features. They are parsimonious, simple to estimate, and imply an ARMA structure for the conditional variance and the realized measure. An empirical application with Dow Jones Industrial Average stocks and an exchange traded index fund shows that a simple Realized GARCH structure leads to substantial improvements in the empirical fit over standard GARCH models that only use daily returns.

Purpose The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility. Design/methodology/approach This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area. Findings The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area. Originality/value This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.