Direction Of Volatility Spillovers Research Articles

Connectedness analysis of oil price shocks, inflation, and exchange rate for the MENA region countries

The relation among energy prices, exchange rates and inflation has been perturbed in the recent past by both the COVID-19 shock, which resulted in a sharp downturn of the economic activity at the beginning of 2020 and the Ukraine crisis, which resulted in sharp increase in energy prices and then inflation at the beginning of 2022. This is the first paper to analyze this relation for the MENA region, conducting an empirical estimation of a VARX model on monthly data from Jan 2010 to Dec 2022 for the main 11 MENA countries. The model includes asymmetric effects, a detailed contagion analysis, conducted with the methodology à la Pesaran - Pick (2007) to identify periods of high volatility, and presents a spillover analysis à la Diebold - Yılmaz (2014), used to measure the connectedness of the three main variables.The novelties of the paper are the construction of a country specific oil portfolio; the asymmetric analysis of oil shocks and contagion; the measurement of the general impulse response spillover, to ascertain the intensity and direction of volatility spillovers. The empirical results show that: (i) the specific oil prices considered for each country induce different effects; (ii) the oil price changes effect depends on the direction and period of the shock; (iii) there are asymmetric contagion effects for exchange rate and inflation only for some countries. Finally, some policy implications are presented based on the spillover connectedness analysis, especially considering the post Covid period.

Volatility Spillover and Directionality in Cryptocurrency and Metal Markets

This article investigates the dynamic relationship between cryptocurrencies and metals, examining the existence and direction of volatility spillovers. While previous studies have explored the relationships between different cryptocurrencies and between base metals and gold, there is a notable gap in understanding the volatility spillover nexus among cryptocurrencies. This study makes a significant contribution by employing the Time-Varying-Parameter-Vector-Autoregressive (TVP-VAR) total connectedness measure to assess the strength of association between these assets. Our analysis employs 10-year daily returns data for three cryptocurrencies (Bitcoin, Litecoin, and Ethereum) and two metals (Gold and Copper). As we witness major economic events worldwide, this study is particularly relevant, as it provides insights into potential hedging opportunities. To comprehend the risk contagion patterns, various measures of partial and dynamic connectedness are computed, supporting the earlier TVP-VAR analysis. The findings indicate that Litecoin and Ethereum exhibit a high level of connectedness, while Bitcoin remains relatively less connected. Among the metals, Gold and Copper demonstrate similar levels of connectedness in certain cases. Notably, there is a significant risk contagion between Litecoin and metals. These results hold essential implications for policy-makers and portfolio managers with different time horizons, offering valuable insights into risk contagion within the cryptocurrency and metal markets. JEL Codes: C32; G15; G17; G41

Exchange Rate and Stock Prices Volatility Connectedness and Spillover during Pandemic Induced-Crises: Evidence from BRICS Countries

This paper investigated exchange rate and stock price volatility connectedness and spillover in Brazil, Russia, India, China, and South Africa (BRICS) during pandemic-induced crises. We first extracted volatility using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then volatility connectedness and spillover were investigated by using (Diebold and Yilmaz, International Journal of Forecasting, 28(1), 57–66, 2012) method. We find that exchange rate volatility and stock return volatilities are connected during pandemic-induced crises. The study also finds volatilities spillover among countries in the sample. Russia has strong volatility connectedness with India in these financial markets. The direction of volatility spillover is from Russia to India. Similarly, Brazil has strong volatility connectedness with South Africa and the direction volatility spillover is from Brazil to South Africa. Finally, China has a weak volatility connection with the remaining BRICS countries. Thus, the volatility transfer in these financial markets and across BRICS countries has economic implications.

Volatility spillover in aquaculture and fisheries markets

While there have been a number of studies investigating the market integration between farmed and wild seafood, the relationship in terms of volatility has received little attention. However, with the high degree of market integration for similar species, one would also expect substantial volatility spillover. In this paper, we investigate the price volatility relationship between seafood from aquaculture and fisheries. Using trade data from 1990 to 2016, we assess both size and direction of volatility spillover over time, allowing us to evaluate the relative importance of both production techniques on seafood price volatility. Our results show a time-varying relationship as the level and direction of volatility spillover changes. Still, our results indicate that wild markets typically transmit price uncertainty to aquaculture markets, thus increasing the volatility in aquaculture markets. In addition, our results indicate that it requires a substantial (negative) supply shock to aquaculture production to shift volatility spillover from aquaculture to wild. These results add to previous studies confirming that prices of aquaculture products are less volatile than those of wild products and, in general, reduce volatility in seafood markets.

Volatility spillovers between south Asian stock markets: evidence from Sri Lanka, India and Pakistan

This study examines the existence, magnitude and direction of volatility spillovers between the Sri Lankan stock market and two other major stock markets in the South Asian region: India and Pakistan. Main stock indices of Sri Lanka, India, and Pakistan are employed as proxies to represent stock markets of each country. Daily data over the period 2nd January 2004 to 23rd September 2014 is used for estimations. Volatility spillovers are modeled through a trivariate BEKK – GARCH (1, 1) model to capture the cross-market effects. There exist bilateral intraday volatility spillovers between Sri Lanka and both markets. It is evident that the intraday effect from Pakistan to Sri Lanka is stronger than the same effect from India to Sri Lanka. However, with respect to overnight volatility spillovers, there is only a unilateral spillover effect from Sri Lanka to India. Evidence for the presence of volatility spillovers between these three South Asian economies makes the tasks of monetary policy makers, investors and fund managers more complicated than they would otherwise have been.

A complex network for studying the transmission mechanisms in stock market

This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market’s volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK-GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks.

Contemporaneous Spillover Effects between the U.S. and the U.K. Equity Markets

AbstractWe use high frequency data and the “identification through heteroskedasticity” approach of Rigobon (2003) to capture the contemporaneous volatility spillover effects between the U.S. and U.K. equity markets. We demonstrate the relevance of taking into account the information present during simultaneous trading hours by comparing the results generated by our structural vector autoregression with those of a traditional reduced‐form vector autoregression. Our findings clearly demonstrate that contemporaneous relations matter and that ignoring them leads to inappropriate conclusions regarding the magnitude and direction of volatility spillover.

Exposing volatility spillovers: A comparative analysis based on vector autoregressive models

We present a comparative analysis of two empirical methods grounded on a common vector autoregressive framework. In this setting, we investigate the time-varying nature and direction of volatility spillovers between some major stock indexes spanning across Europe, China and US. We find evidence that drawing on partial Granger causality brings more robust results than relying on the information provided by generalized impulse responses, especially when there is uncertainty about what other relevant factors need to be modelled.

Impact of Futures Trading on Spot Markets: An Empirical Analysis of Rubber in India

The present study investigates the impact of futures trading on spot markets of rubber in India. The study focuses on the price discovery role of futures, direction of volatility spillovers, and the relationship between the futures trading activity and the spot price volatility. The analysis using the Co-integration and Error Correction Model suggests that there is a stronger information flow from the futures to spot markets, indicating price discovery in futures. The results of Granger Causality tests show the existence of a bidirectional volatility spillover in the two markets and that futures trading activity is both a cause and consequence of spot volatility.

Empirical Analysis of Volatility Spillover Effects in International Financial Markets

Volatility spillover effect in international financial markets is one of the principal issues that widely attract academic and industrial scholars’ attention. Through constructing a binary GARCH-BEKK model, this study empirically tests the volatility effects among stock market, gold market, WTI crude oil futures market and spot market, and concludes that there is bidirectional volatility spillover effect between stock market and gold market, WTI crude oil futures market and spot market, and unidirectional volatility spillover effect among other markets. The test results indicate the direction of volatility spillover and the information conduction path in the international financial market, and it has an important role in enlightening and guiding investors.