Volatility Transmission Effects Research Articles

Oil Price Fluctuation And Exchange Rate In Nigeria: Is There A Volatility Transmission Effect

Over the period 1980-2022, we examined the transmission effect of oil prices on Nigeria's exchange rate. E-GARCH (Exponential GARCH) model is employed in this study. We used Augmented Dickey Fuller to determine a unit root, integrating exchange rates, crude oil prices, external reserves, GDP, inflation, and interest rates to one I(1) and zero I(0). We used Johansen Co-integration to determine long-term relationships. Interest rates, inflation, and crude oil prices all correlated positively with each other. A statistically insignificant result is shown by the variance equation, indicating there is no correlation between crude oil prices and exchange rates. The Nigerian exchange rate is not affected by volatility transmission or leverage due to fluctuations in crude oil prices. According to the study, governments should take monetary policy measures to stabilize exchange rates during unpredictable oil price fluctuations. It is necessary to adjust the naira exchange rate when oil prices fluctuate.

Mutual volatility transmission between assets and trading places

Abstract This article proposes a framework to model the mutual volatility transmission between multiple assets and multiple trading places in different time zones. The model is estimated using a dataset containing daily returns of three stock indices – the MSCI Japan, the EuroStoxx 50, and the S&P 500 – each traded at three major trading places: the stock exchanges in Tokyo, London, and New York. Strong volatility transmission effects can be observed between New York and Tokyo, whereas current volatility in New York mostly depends on past volatility in New York. For the assets in consideration, spillovers are strong across trading zones, but weak across assets, suggesting a close connection between market places but only a loose volatility link between international stock indices. Volatility impulse response functions indicate a long-lasting and comparably large response of volatility in Tokyo, whereas they suggest a quicker volatility decay in London and New York.

Intraday price discovery and volatility transmission between the dual-listed stock index futures and spot markets – new evidence from India

PurposeThis study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.Design/methodology/approachThis study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.FindingsThis study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.Practical implicationsThese findings can potentially help market participants, policymakers, stock exchanges and regulators.Originality/valueUnlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

Are Sustainability Indices Infected by the Volatility of Stock Indices? Analysis before and after the COVID-19 Pandemic

Considering the growing importance of sustainable investments worldwide, we explore the volatility transmission effects between the EURO STOXX Sustainability Index and the stock market indexes of its stocks. Using daily index return data, during 2000–2022, covering the COVID-19 pandemic, Multivariate Generalized Auto-Regressive Conditional Heteroskedasticity (MGARCH) models are used to explore if volatility effects of the stock indices felt during the pandemic implied any evolution in the effects already felt between the volatilities existing in these stock indices and the effects of stock market indices’ volatility over the sustainability index. Results point to the great dependence that the sustainability index has on stock index movements. The volatility felt in stock indices during the pandemic period did not become decisive in reversing a previous correlation trajectory between the stock market and sustainability indexes. Provided that sustainability is not observed exclusively in financial and economic terms, but in a triple bottom line context (including the social and environmental sides), we should not verify a high influence of stock market indexes over the sustainability index, as the results point out. Policymakers and investors should be aware of the high influence and take measures to turn the sustainability index more independent.

Volatility spillover analysis between stocks and exchange rate markets in short and long terms in East European and Eurasian countries

PurposeThis paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.Design/methodology/approachResearch process involves creation of transitory and permanent volatilities via optimal component generalized autoregressive heteroscedasticity (CGARCH) model, while these volatilities are subsequently embedded in Markov switching model.FindingsThis study’s results indicate that bidirectional volatility transmission exists between the markets in the selected countries, whereas the effect from exchange rate to stocks is stronger than the other way around in both short-term and long-term. In particular, the authors find that long-term spillover effect from exchange rate to stocks is stronger than the short-term counterpart in all countries, which could suggest that flow-oriented model better explains the nexus between the markets than portfolio-balance approach. On the other hand, short-term volatility transfer from stock to exchange rate is stronger than its long-term equivalent.Practical implicationsThis suggests that portfolio-balance theory also has a role in explaining the transmission effect from stock to exchange rate market, but a decisive fact is from which direction spillover effect is observed.Originality/valueThis paper is the first one that analyses the volatility nexus between stocks and exchange rate in short and long term in the four East European and two Eurasian countries.

The effect of the COVID-19 outbreak on hospitality and tourism stock returns in Thailand

ABSTRACT This study examines the volatility transmission effects between stock returns and the growth rate of total confirmed COVID-19 cases by using daily data of the hospitality and tourism industry taken from the Stock Exchange of Thailand (SET) index. Augmented Dicky–Fuller (ADF), Phillips–Perron (PP), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), Elliott–Rothenberg–Stock (ERS) and Ng–Perron (NP) unit root tests were used to test, for both series are stationary. The BEKK-GARCH methodology was employed to formulate conditional variance-covariance equations. The results reveal that the pandemic interacts negatively with stock returns from the hospitality and tourism industry. Stock market returns are significantly negatively associated with daily growth in total confirmed COVID-19 cases.

Is non-fungible token pricing driven by cryptocurrencies?

In early 2021, non-fungible tokens (NFT) became the first application of blockchain technology to achieve clear public prominence. NFTs are tradeable rights to digital assets (images, music, videos, virtual creations) where ownership is recorded in smart contracts on a blockchain. Given the NFT market emerged out of cryptocurrencies, we explore if NFT pricing is related to cryptocurrency pricing. A spillover index shows only limited volatility transmission effects between cryptocurrencies and NFTs. But wavelet coherence analysis indicates co-movement between the two sets of markets. This suggests that cryptocurrency pricing behaviours might be of some benefit in understanding NFT pricing patterns. However, the low volatility transmissions also indicate that NFTs can potentially be considered as a low-correlation asset class distinct from cryptocurrencies.

The risk transmission of COVID-19 in the US stock market

ABSTRACT This paper studies volatility transmission effects between the US stock market and the COVID-19. Using BEKK-multivariate GARCH model, we find the US stock market volatility depends both its own past shocks and past COVID-19 shocks. Further, we find the US stock market volatility is positively affected by the death rate (bad news) while the recovered rate (good news) has a negative impact on the US stock market volatility. In addition, we find there is an asymmetric volatility impact of COVID-19 on the US stock market: the bad news affects the current US stock market much more than the good news. Our fixed effect panel regression results support the volatility spillover effects.

Is non-fungible token pricing driven by cryptocurrencies?

In early 2021, non-fungible tokens (NFT) became the first application of blockchain technology to achieve clear public prominence. NFTs are tradeable rights to digital assets (images, music, videos, virtual creations) where ownership is recorded in smart contracts on a blockchain. Given the NFT market emerged out of cryptocurrencies, we explore if NFT pricing is related to cryptocurrency pricing. A spillover index shows only limited volatility transmission effects between cryptocurrencies and NFTs. But wavelet coherence analysis indicates co-movement between the two sets of markets. This suggests that cryptocurrency pricing behaviours might be of some benefit in understanding NFT pricing patterns. However, the low volatility transmissions also indicate that NFTs can potentially be considered as a low-correlation asset class distinct from cryptocurrencies.

Nonlinear bidirectional multiscale volatility transmission effect between stocks and exchange rate markets in the selected African countries

This paper investigates the multiscale bidirectional volatility spillover effect between the national stocks and exchange rate markets in four African countries – Nigeria, South Africa, Egypt and Morocco. Our computations involve the wavelet transformation of the empirical series, creation of the regime-dependant conditional volatilities via MS-GARCH model and measurement of the volatility spillover effect in the quantile regression framework. We find an evidence of the bidirectional volatility spillover effect, but the volatility impact from exchange rate market to stock market is stronger in all the African countries, except Nigeria. Regarding the direction from stocks to exchange rate, we report that volatility spillover effect is the strongest in South Africa, because Johannesburg stock exchange is the most developed and liquid market. As for the reverse direction, the spillover effect is recorded in longer time-horizons in the Egyptian and Moroccan cases, which indicates to flow-oriented model, while for South Africa, the effect is found in shorter time-horizons, which is in line with the portfolio-balance theory. In South Africa, investors should protect themselves against exchange rate risk in shorter time-horizons, in Morocco and Egypt in longer time-horizons, while in Nigeria, hedging against exchange rate is not needed.

Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

Assessing the multiscale “meteor shower” effect from oil to the central and eastern European stock indices

AbstractThis paper investigates the idiosyncratic volatility spillover effect from the Brent oil futures market to the 11 stock markets of Central and Eastern European economies. As volatility proxies, we use regime‐switching conditional volatilities, obtained from two‐states MS‐GARCH model. In order to determine the level of this effect in different market conditions and in different time‐horizons, we combine wavelet methodology with the quantile regression approach. Our results indicate that the volatility spillover effect is not particularly strong across the countries and the wavelet scales, except in those conditions when stock market volatility is exceptionally high. Also, the wavelet‐based quantile parameters report that the volatility transmission effect gradually subsides with the flow of time, and it applies for the majority of the indices. Romanian BET index experiences the strongest volatility spillover effect from oil in conditions when Romanian stock market is under extreme stress. The reason for this finding probably lies in the facts that Romania is the largest oil and gas producer among all CEECs, and oil and gas markets tend to comove strongly. Based on findings of wavelet quantile parameters and wavelet correlations, we can conclude that hedgers and portfolio managers can build their portfolio strategies, combining Brent oil futures with the CEE indices.

EFFECTS OF VOLATILITY AMONG COMMODITIES IN THE LONG TERM: ANALYSIS OF A COMPLEX NETWORK

The large inflow of investment capital in critical periods sparked a debate about the extent to which these speculative bubbles affect asset volatility and how (and what extent) these volatilities are transmitted between them. In periods of greater uncertainty, commodity futures markets may receive and/or send two types of volatility spillovers: intergroup of assets and/or intragroup of assets. We tested for the period from March 3, 2000 to May 4, 2017, which of the two effects prevailed and in which group of assets was more intense. We concluded that the most relevant volatility transmission effects (measured by Diebold–Yilmaz indices) occurred intragroup of assets — corn, wheat, soy, oats and rice. These assets make up the main cluster of a commodities complex network. Thus, we detected and measured using network approach that the most significant effects was over the years of the Great Recession (2007–2009) and the peak of the European Sovereign Debt Crisis (2010–2012).

Asymmetric volatility transmission in Japanese stock market in the presence of structural breaks

This research shows that accounting for structural breaks within the asymmetric GJR-GARCH model reduces volatility persistence in stock and oil returns. More importantly, we ascertain that good news has less impact on current volatility if sudden changes are accounted for, while bad news has more impact in all cases with an exception of Nikkei225. Our findings are in line with previous studies and highlight that it is plausible to include structural breaks in asymmetric GARCH models to model volatility dynamics more precisely. The results of the univariate analysis provide evidence of bi-directional volatility spillover effects between the Japanese stock market and the rest of the markets, independently of the model choice. Thus, neglecting structural breaks in variance may lead to different results but not to the extent of distortion of the direction of volatility transmission effects. The latter finding does not hold true under the multivariate context highlighting the power difference between the CCF approach and the BEKK-GARCH models. Finally, we ascertain that data frequency may also affect the direction of the volatility spillovers among the markets.

The volatility transmission between crude oil market and tanker freight market

International crude oil and tanker shipping markets interrelate closely. Crude oil prices and tanker freight rates always fluctuate drastically, however the volatility transmission mechanism between the two markets have been ignored. The purpose of this study is to examine volatility transmission effects between the two markets by applying the BEKK-GARCH model on analysing Brent oil prices and Baltic Dirty Tanker Index. The index range was set from 18th January 2007 to 9th November 2015 covering the periods of financial crisis and oil price fall. Results show that volatility is mainly transmitted from crude oil market to tanker freight market from a long-term perspective, but not vice versa. Some bi-directional effects were found in tanker freight market that lagged variances and could affect the current variance in a counterpart market, regardless of the volatility transmission for some certain periods. The findings could help the practitioners better understand the market dynamics.

Relationship between the United States housing and stock markets: Some evidence from wavelet analysis

We revisit the relationship between the United States housing and stock markets in time-frequency domain. Earlier research does not have satisfactory results on the interactions between the two markets because traditional methods average different relationships in time domain only. Our novel and informative wavelet-based multi-resolution analyzes indicate that the US housing and stock markets are at best moderately integrated and with scale-dependent co-movement, connectivity and causality. The interplay between the US housing and stock markets is stronger in the long run, with the two asset markets being bilaterally causally linked and have stronger return and volatility transmission effects. Finally, we demonstrate that the decomposition of the relationship between the real estate and stock markets over the different scales has important implications in studying the optimal portfolio weight and the hedge ratio in risk management.

The asymmetric high-frequency volatility transmission across international stock markets

We construct a MHAR-DCC model to investigate the high-frequency volatility transmission across international stock markets. We use the overnight volatility estimator to eliminate the effects of non-synchronous trading problem. We analyze the asymmetric volatility transmission effects across the international stock markets. The empirical results suggest that the periods when the total spillover index increases to high levels correspond to the periods when the market volatility is high. The volatility transmission effect in the US and Singapore stock markets exhibit the normal leverage effect, while the volatility transmissions of Japanese and Hong Kong stock markets exhibit the reverse leverage effect.

Asymmetric price transmission effect of corn on hog: evidence from China

In this study, we used monthly corn price and hog price data from January 2000 to June 2015 to conduct an empirical analysis based on a smooth transition regression (STR) model. The analysis confirms and explains the asymmetric transmission mechanism and process of the smooth transformation of corn prices to hog prices and measures the mechanism conversion threshold. Using the smooth transformation mechanism and its threshold as its foundations, this study breaks up continuous smooth transfer price volatility transmission effects into completely linear, not completely linear, and nonlinear mechanism states. Based on these states, the influence of corn price on hog price fluctuation is attributed to cost-push inflation, risk stabilisation effects, and the coexistence of cost-push and risk-stabilisation effects from the perspective of adaptive expectations.

Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets

Price and volatility transmission effects between European Union (EU) and World skimmed milk powder (SMP) prices, as well as those between both SMP series, soybeans and crude oil prices from 2004 to 2014 were analysed using a vector error correction model combined with a multivariate GARCH model. The results show significant transmission effects between EU and World SMP prices, but no significant transmission effects from soybeans or crude oil to either of the SMP prices. For policymakers and modellers, these results indicate the need to consider World SMP prices when considering EU prices. On the other hand, the finding of no transmission effects from soybean to SMP prices reduces the opportunity for a successful cross-hedging for dairy commodities using well-established soybean derivative markets.

Spillover Effects of Real and Nominal Uncertainties in India

This paper examines the spillover effects and the causality between inflation, output growth and its uncertainties for India. Using monthly data for the period from April 1980 to April 2011, we estimated a bi-variate GARCH in mean with BEKK representations. This study differs from the earlier works where the parameters in the BEKK representations are estimated individually and the inferences are drawn on the basis of the individual lagged variance, covariance, and error terms from the respective equations. The empirical evidence suggests that inflation uncertainty seems to have significant negative impact on output growth and positive impact on output uncertainty and there is a positive influence of output uncertainty on the inflation. More importantly, there are spillovers and volatility transmission effects between the macroeconomic uncertainties where the volatility in output growth is significantly influenced by the shocks and volatility in inflation.