Spillover analysis across FinTech,ESG, and renewable energy indices before and during the Russia–Ukraine war: International evidence

This study is epicentral to analyze the impact of the Russia–Ukraine war on the financial markets, specifically focusing on the connectedness and spillover dynamics of FinTech, Environmental, Social, and Governance (ESG), renewable energy, gold, and Morgan Stanley Capital International (MSCI) indices in developed and emerging countries. Data are collected from Thomson Reuters, ranging from May 8, 2020, to May 11, 2022, and a time-varying parameter vector autoregression (TVP-VAR) and the dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) t-Copula (DCC-GARCH t-Copula) are used to analyze the data. The results show that FinTech, ESG, and MSCI are net transmitters in developed countries, whereas gold and renewable energy are net receivers pre- and during war periods. ESG and MSCI are net transmitters in emerging countries, while FinTech, renewable energy, and gold become net receivers in both periods. The hedging ratio sheds light on the costs and weights of efficient pair investments that might change in the context of each region and under the combined scenario. The study has important implications for merchant bankers, policymakers, investors, hedgers, and risk managers.     

Predicting volatility using the Markov-switching multifractal model: Evidence from S&P 100 index and equity options

In this paper, we evaluate the performance of the ability of Markov-switching multifractal (MSM), implied, GARCH, and historical volatilities to predict realized volatility for both the S&P 100 index and equity options. Some important findings are as follows. First, we find that the ability of MSM and GARCH volatilities to predict realized volatility is better than that of implied and historical volatilities for both the index and equity options. Second, equity option volatility is more difficult to be forecast than index option volatility. Third, both index and equity option volatilities can be better forecast during non-global financial crisis periods than during global financial crisis periods. Fourth, equity option volatility exhibits distinct patterns conditional on various equity and option characteristics and its predictability by MSM and implied volatilities depends on these characteristics. And finally, we find that MSM volatility outperforms implied volatility in predicting equity option volatility conditional on various equity and option characteristics.     

Interrelations in market fears of U.S. and European equity markets

We examine interdependence between the implied volatilities of U.S. and five European markets in an integrated multivariate system that allows interactions in the first and second moments of volatility processes. Our results find significant interactions in the variance-covariance matrix of VIX and European volatilities which persist and facilitate risk transmission. Changes in U.S. and Eurozone volatilities are important drivers of risk shocks in European markets. VIX and European volatilities have predictive ability for each other. Further, VIX shocks contribute significantly to the prediction error of European risk shocks, but not vice versa. Risk transmission from U.K. markets to U.S. and European markets intensified around the Brexit vote. Also, VIX shocks added significantly more to European risks during the global financial crisis. Our results highlight the potential weakness of risk transmission models that ignore the second-moment risk transmission channel and have implications for volatility trades, portfolio diversification strategies, and hedging the cross-market risks.     

The dynamic impact of oil price shocks on the stock market and the USD/RMB exchange rate: Evidence from implied volatility indices

Using daily data from March 16, 2011, to September 9, 2019, we explore the dynamic impact of the oil implied volatility index (OVX) changes on the Chinese stock implied volatility index (VXFXI) changes and on the USD/RMB exchange rate implied volatility index (USDCNYV1M) changes. Through a TVP-VAR model, we analyse the time-varying uncertainty transmission effects across the three markets, measured by the changes in implied volatility indices. The empirical results show that the OVX changes are the dominant factor, which has a positive impact on the USDCNYV1M changes and the VXFXI changes during periods of important political and economic events. Moreover, USDCNYV1M changes are the key factor affecting the impact of OVX changes on VXFXI changes. When the oil crisis, exchange rate reform, and stock market crash occurred during 2014–2016, the positive effects of uncertainty transmission among the oil market, the Chinese stock market, and the bilateral exchange rate are significantly strengthened. Finally, we find that the positive effects are significant in the short term but diminish over time.     

Modelling Regime‐Specific Stock Price Volatility*

Single‐state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state‐dependent behaviour of volatility nor explain why the equity index skew persists into long‐dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state‐dependent leverage effect to NM‐GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time‐varying volatility risk premia. An empirical study on European equity indices identifies two‐state asymmetric NM‐GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean‐reversion and leverage effects during crash markets are quite different from those in the stable regime.     

Shock and volatility spillovers among equity sectors of the Gulf Arab stock markets

The major objectives of this study are twofold. The first objective is to examine the dynamic volatility and volatility transmission in a multivariate setting using the VAR(1)–GARCH(1,1) model for three major sectors, namely, Service, Banking and Industrial/or Insurance, in four Gulf Cooperation Council (GCC)’s economies (Kuwait, Qatar, Saudi Arabia and UAE). The second is to use the models’ results to compute and analyze the optimal weights and hedge ratios for two-sector portfolio holdings, comprised of the three sectors for each country. The results suggest that past own volatilities matter more than past shocks and there are moderate volatility spillovers between the sectors within the individual countries, with the exception of Qatar. Moreover, the values for ratios of hedging long positions with short positions in the GCC sectors are smaller than those for the US equity sectors. The optimal portfolio weights favor the Banking/financial sector for Qatar, Saudi Arabia and UAE and the Industrial sector for Kuwait.     

Region-wide connectedness of Asian equity and currency markets

This study systemically analyzes the dynamics of interdependence between the Asian equity and currency markets. The novelty of our study is that unlike other studies that explore either co-movements among equity markets or co-movements among currency markets, we pay particular attention to the interdependence between the two in terms of both return and volatility connectedness. We find that the contribution of crossspillovers between the Asian equities and currencies is substantial for the region-wide connectedness of both the returns and volatilities. We also find that the short-term spillovers are far more important for the return spillovers, while the long-term spillovers are far more important for the volatility spillovers, presumably reflecting the long-lasting effects of volatility shocks. All the results consistently underline the pivotal role of cross-interdependence between equity and currency markets, both as channels for integrating Asian financial markets and as sources of financial contagion across these markets. Our findings will provide useful guidance for portfolio risk management to adopt better hedging strategies for foreign exchange risks involved in the international investment of Asian equities.     

MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES

We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long‐run from short‐run components. We allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate nonparametrically. For the short‐run dynamics, we use a GJR‐GARCH model for the conditional variances and augmented DCC models for the conditional correlations. We also introduce exogenous variables to account for congestion and delivery date effects in short‐term conditional variances. We find different correlation dynamics for long‐ and short‐term contracts and the new model achieves higher forecasting performance compared \to a standard DCC model. Copyright © 2012 John Wiley & Sons, Ltd.     

Value investing versus other investment strategies: A volatility spillover approach and portfolio hedging strategies for investors

Motivated by the incessant demand for portfolio diversification, this study examines the connectedness between value and diverse types of stocks (growth, momentum, ESG, high beta, classic S&P 500, volatility). The applied methodology encompasses the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (2012) framework for the period from 03/31/2011 to 03/31/2021. Results show moderate volatility transmissions among the sampled assets, which tend to escalate during periods of turmoil, such as the European Sovereign Debt Crisis, the plunge in oil prices and the COVID-19 outbreak. Growth and ESG stocks play an indispensable part in the transmission mechanism. Moreover, we investigate the hedging ability of value stocks within a portfolio containing other stocks, by estimating hedge ratios and optimal weights with the usage of conditional variance estimates (DCC-GARCH). The empirical findings reveal that value stocks can adequately hedge against the risk deriving from the volatility of the remaining investment instruments, especially in the case of high beta and volatility stocks. Thus, this analysis provides portfolio managers and investors with valuable insights in order for them to hedge their stock portfolios effectively.     

Is there a risk–return trade‐off? Evidence from high‐frequency data

This paper examines the intertemporal relation between risk and return for the aggregate stock market using high‐frequency data. We use daily realized, GARCH, implied, and range‐based volatility estimators to determine the existence and significance of a risk–return trade‐off for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macro‐economic variables associated with business cycle fluctuations. We also analyze the risk–return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high‐frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index). Copyright © 2006 John Wiley & Sons, Ltd.     

Modelling the conditional volatility of commodity index futures as a regime switching process

Commodity index futures offer a versatile tool for gaining different forms of exposure to commodity markets. Volatility is a critical input in many of these applications. This paper examines issues in modelling the conditional variance of futures returns based on the Goldman Sachs Commodity Index (GSCI). Given that commodity markets tend to be ‘choppy’ (Webb, 1987 ), a general econometric model is proposed that allows for abrupt changes or regime shifts in volatility, transition probabilities which vary explicitly with observable fundamentals such as the basis, GARCH dynamics, seasonal variations and conditional leptokurtosis. The model is applied to daily futures returns on the GSCI over 1992–1997. The results show clear evidence of regime shifts in conditional mean and volatility. Once regime shifts are accounted for, GARCH effects are minimal. Consistent with the theory of storage, returns are more likely to switch to the high‐variance state when the basis is negative than when the basis is positive. The regime switching model also performs well in forecasting the daily volatility compared to standard GARCH models without regime switches. The model should be of interest to sophisticated traders who base their trading strategies on short‐term volatility movements, managed commodity funds interested in hedging an underlying diversified portfolio of commodities and investors of options and other derivatives tied to GSCI futures contracts. Copyright © 2001 John Wiley & Sons, Ltd.     

Time-frequency co-movement and network connectedness between green bond and financial asset markets: Evidence from multiscale TVP-VAR analysis

This study examines the time-frequency co-movement and network connectedness between green bonds and other financial assets in China. We propose wavelet coherence and multiscale TVP-VAR to explore the time-frequency co-movement and spillover connectedness. The empirical results are as follows. First, green bonds positively co-move with conventional bonds across time scales and negatively co-move with stocks and commodities. Second, there is a significant network connectedness of green bonds with conventional bonds in the short term, and the connectedness with stocks and commodities gradually strengthens with the increase in time scales. Third, the dynamic spillover between green bonds and other assets is much greater in the long and medium terms than in the short term. Finally, under crisis shocks, the spillovers spike temporarily in the short term, while they are persistent and at a high level in the long term. Overall, some practical implications are proposed for investors and policymakers.     

Volatility linkages in the spot and futures market in Australia: a copula approach

To better characterize the dependence structure of the joint returns distribution, we propose to blend copula functions with Asymmetric GARCH (AGARCH) models, which are allowed for generalized error distribution. We model the copula’s marginals by the AGARCH processes that can differentiate between the impacts of positive and negative shocks on the returns volatility while taking the large kurtosis of the returns into account. An application of the procedure is elaborated on the All Ordinaries Index and its corresponding Share Price Index on future contracts in Australia. The findings reveal that the two spot and future markets show a strong right tail dependence but no left tail dependence. This provides a very useful knowledge for the risk management and hedging in futures markets.     

Stock returns and volatility: Evidence from the Athens Stock market index

This paper investigates the volatility of the Athens Stock excess stock returns over the period 1990–1999 through the comparison of various conditional hetero-skedasticity models. The empirical results indicate that there is significant evidence for asymmetry in stock returns, which is captured by a quadratic GARCH specification model, while there is strong persistence of shocks into volatility.     

Intraday Volatility Spillover between the Shanghai and Hong Kong Stock Markets—Evidence from A+H Shares after the Launch of the Shanghai-Hong Kong Stock Connect

Using minute data of eligible A+H stocks under the Shanghai-Hong Kong Stock Connect (SHHKSC), we investigate the volatility spillover between the Shanghai and Hong Kong stock markets based on a generalized autoregressive conditional heteroskedasticity-X (GARCH-X) model with four exogenous variables, namely, volatilities of the corresponding stocks on the other market, volatilities of the indexes of both stock markets, and volatilities of the correlated stocks, which are selected using the dynamic conditional correlation model and bootstrap approach. Results show that after the launch of the SHHKSC, volatility spillovers are significant in both directions almost all the time, and the volatility spillover between the two stock markets tends to be larger when bidirectional capital flows under the SHHKSC increase or when important financial events occur. We also analyze the influences of the volatilities of correlated stocks and industries on the volatility spillover and volatilities of A+H stocks. The bidirectional volatility spillovers between Shanghai and Hong Kong stock markets do not change qualitatively after incorporating the volatilities of correlated stocks and industries in the GARCH-X model. Moreover, the average volatilities of the correlated stocks are shown to have significant influences on the volatilities of individual A+H stocks, and the influences increase when the local stock market shows a sharp rise or fall. Compared with the market indexes, the correlated stocks could be regarded as a more important and indispensable factor for individual A+H stocks’ volatilities modeling, which may carry more information than the industry.     

Thresholds,news impact surfaces and dynamic asymmetric multivariate GARCH

Dynamic Asymmetric Multivariate GARCH (DAMGARCH) is a new model that extends the Vector ARMA‐GARCH (VARMA‐GARCH) model of Ling and Mc Aleer (2003) by introducing multiple thresholds and time‐dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset‐specific shocks and common innovations by partitioning the multivariate density support. This article presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi‐maximum likelihood estimators. The article also presents an empirical example to highlight the usefulness of the new model.     

波动率预测:GARCH模型与隐含波动率

在预测未来波动率时,究竟是基于历史数据的时间序列模型还是基于期权价格的隐含波动率模型效率更高?本文对香港恒生指数期权市场所含信息的研究发现,在预测期限较短(一周)时,GARCH(1,1)模型所含信息较多,预测能力最强,但在预测较长期限(一个月)时,隐含波动率所含信息较多,预测能力较强。同时,期权市场交易越活跃,所反映的信息就越全面,隐含波动率的预测能力也就越强。     

Asymmetric volatility connectedness among U.S. stock sectors

This paper examines the dynamic asymmetric volatility connectedness among ten U.S. stock sectors (Consumer Goods, Consumer Services, Financials, Health Care, Materials, Oil and Gas, Technology, Telecom, Real Estate Investment Trust (REIT), and Utilities). We use the methodology of Diebold and Yilmaz (2012, 2014, 2016) and the realized semivariances introduced by Baruník et al. (2017) to five-minute data. The results show evidence of time-varying spillovers among U.S. stock sectors which is intensified during economic, energy and geopolitical events. Moreover, the spillovers under bad volatility dominates the spillovers under good volatility, supporting evidence of asymmetry. Financials, Materials, Oil and Gas, REIT, Technology, Telecom and Utilities are net receiver of spillover under good volatility (positive semivariance). In contrast, Oil and Gas shift to net contributor of spillover under bad volatility (negative semivariance). Moreover, the connectedness network among sectors exhibits asymmetric behaviors. These results have important implications for risk management.     

Transient fads and the crash of ′87

Using a fad model with Markov-switching heteroscedasticity in both the fundamental and fad components (UC-MS model), this paper examines the possibility that the 1987 stock market crash was an example of a short-lived fad. While we usually think of fads as speculative bubbles, what the UC-MS model seems to be picking up is unwarranted pessimism which the market exhibited with the OPEC oil shock and the '87 crash. Furthermore, the conditional variance implied by the UC-MS model captures most of the dynamics in the GARCH specification of stock return volatility. Yet unlike the GARCH measure of volatility, the UC-MS measure of volatility is consistent with volatility reverting to its normal level very quickly after the crash.     

Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations

ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed innovations, we characterize the limiting distribution of an estimator of the conditional value-at-risk (VaR), which corresponds to the extremal quantile of the conditional distribution of the GARCH process. We propose two methods, the normal approximation method and the data tilting method, for constructing confidence intervals for the conditional VaR estimator and assess their accuracies by simulation studies. Finally, we apply the proposed approach to an energy market data set.