Multivariate Hyper-Rotated GARCH-BEKK

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 Econometrics 179: 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.

Searching for hedging and safe haven assets for Indian equity market – a comparison between gold, cryptocurrency and commodities

Purpose This paper aims to evaluate the hedging and safe haven properties of gold, cryptocurrency and commodities against the Indian equity market. Design/methodology/approach First, the authors estimate the hedging and safe haven abilities of gold, cryptocurrency and commodities for the Indian stock market and further verify whether such properties vary across the broad stock market indices and over the different degrees of market volatility. Second, the authors use the multivariate GARCH framework to calculate the dynamic hedge ratios and hedging efficiencies to compare the hedging properties of the alternative asset classes. Third, the authors verify the robustness of the general findings during the recent crisis emanating from the outbreak of the COVID-19 pandemic. Findings Gold, cryptocurrency and most commodities have significant hedging abilities. Only natural gas, crude oil and aluminum, on the other hand, have safe haven property. Neither gold nor cryptocurrency qualifies as a safe haven asset. On the other hand, the financialization of the Indian commodities market provides a significant dividend to investors in terms of hedging and safe haven capabilities. The authors find the least negative hedge ratio and the highest positive hedging effectiveness for the stock-crude oil and stock-natural gas portfolios. The central observations of the paper remain immune to the COVID crisis. Originality/value Focusing on the Indian equity market, the paper compares the diversification abilities of traditional assets like gold with those of the modern class of assets, including cryptocurrency and other commodities.

Relationships among the Fossil Fuel and Financial Markets during the COVID-19 Pandemic: Evidence from Bayesian DCC-MGARCH Models

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.

Betas in the time of corona: a conditional CAPM approach using multivariate GARCH model for India

PurposeThis paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.Design/methodology/approachA conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.FindingsThe results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.Originality/valueThis is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.

Money and Foreign Exchange Markets Dynamics in Nigeria: A Multivariate GARCH Approach

This study examines money market and foreign exchange market dynamics in Nigeria by estimating the dynamic correlation and volatility spillovers between Nigeria Naira/US Dollar Bureau De Change (BDC) exchange rate and interbank call rate with data from January 2007 to August 2019. The study employs a dynamic conditional correlation form of GARCH model (DCC-GARCH) to access the nature of correlation, while an unrestricted bivariate BEKK-GARCH (1, 1) form of multivariate GARCH model is utilized to investigate shocks and volatility spillover of the rates. The estimated DCC-GARCH (1, 1) reveals that interest rate and exchange rate are dynamically linked negatively, suggesting that exchange rate (or interest rate) is inversely sensitive to interest rate (or exchange rate) in Nigeria. This result was substantiated by the estimated BEKK-GARCH(1, 1) model. Furthermore, the effects of news (shocks spillover) are bi-directional across the markets. However, volatility spillover is unidirectional, from exchange rate to interest rate, suggesting that, calming the volatility in foreign exchange market does guarantee moderation of volatility in the money market, whereas the reverse is not the case. The results underscore the growing influence of foreign exchange market in the financial space of the Nigerian economy. Thus, the study recommends that foreign exchange policies aimed at maintaining exchange rate stability should be sustained, having found exchange rate to be more effective in moderating interest rate volatility in Nigeria.

Multivariate Garch Analysis of Selected Nigerian Economic Data

Aims: The aim of this study is to examine multivariate GARCH modeling of selected Nigerian economic data. Study Design: The study used monthly data of Nigerian crude oil prices (dollar Per Barrel), consumer price Index rural, maximum lending rate and prime lending rate. Methodology: This work covers time series data on crude oil prices, consumer price Index rural, maximum lending rate and prime lending rate extracted from Central Bank of Nigeria (CBN) from 2000 to 2019. In attempt to achieve the aim of the study, quadrivariate VECH and DCC model were applied. Results: The results confirmed that returns on economic data were correlated. Also, diagonal multivariate VECH model confirmed one of the properties that it must be ‘positive semi-definite’, And the DCC confirmed also the positive-definite conditional-variance. Conclusion: From the results obtained, it was confirmed that there exists a strong confirmation of a time-varying conditional covariance and interdependence among Nigeria economic data. As for cross-volatility effects, past innovations in crude oil price have utmost control on future volatility of returns on economic data. It was also confirmed that time varying covariance displays among these economic data and lower degree of persistence and based on Model selection criteria using the Akaike information criteria (AIC) has 17.485 for diagonal VECH while for DCC has 17.509 AIC which makes VECH model better fitted.

Modeling Mediterranean Stock Markets Volatility with Univariate and Multivariate Approaches

In our study we use the univariate and multivariate GARCH models to analyze the volatility behavior of the daily data of four Mediterranean stock markets (Morocco, Turkey, Spain, and France) spanning the period 2000-2020. We find a strong evidence of persisting of volatility in each of these markets. Results also indicate that both the univariate and the multivariate approaches capture well the ARCH and GARCH effects. We analyze the conditional covariances, and co-volatility spillovers between the Moroccan stock market and the three other Mediterranean stock markets. In order to study co-volatility spillovers, our work is built on the diagonal BEKK model especially the conditional covariances.

Broker-dealer leverage volatility and the US stock prices

Purpose Broker-dealer leverage volatility increases during booms and crisis periods, but its impact on stock prices is relatively unexplored. This paper aims to investigate whether broker-dealer leverage volatility is a key driver for stock prices. Design/methodology/approach This paper collects the US quarterly data of broker-dealer book leverage and three leading stock market indicators (S&P 500, DJIA and Nasdaq) for the period of 1967–2018. The research uses a multivariate GARCH-in-mean VAR to examine the impact of leverage volatility on each of the stock market indicators. A split-sample analysis (pre-1990 and post-1990) has also been performed to show the robustness of the result. Findings The research finds that broker-dealer leverage volatility does not have any significant impact on stock prices. Originality/value Broker-dealers are important financial intermediaries, and there is a huge literature exploring the relationship between their leverage and asset prices. But, the relationship between broker-dealer leverage volatility and asset prices is not explored yet. This study fills the gap and provides the first evidence that broker-dealer leverage volatility does not play any major role in the theory of stock pricing. The research proposes that the stock holding decisions of the investors should depend only on the first moment of leverage and not on the second moment of leverage. The study concludes that high broker-dealer leverage volatility is not a sinister signal for the US stock market.