Volatility Interdependence Research Articles

Unravelling the return and volatility interdependencies between Indian equity ETFs and benchmark indices

Unravelling the return and volatility interdependencies between Indian equity ETFs and benchmark indices

Returns and Asymmetric Volatility Interdependencies between Sustainable and Conventional Stock Indices: Evidence from Developed and Emerging Countries

The study investigates the co-integration and asymmetric volatility interdependence between the sustainable and conventional stock indices of emerging and developed countries. Daily closing values are used from 1st January 2015 to 15th July 2022. The study employs ARDL and VAR-Granger causality model to examine long-run association and cause-effect relation, respectively. Moreover, the Bivariate EGARCH model is also used to analyse return and asymmetric volatility spillover between two indices. The analysis reveals no evidence of co-integration between sustainable and conventional indices. However, bidirectional causality exists in all countries except the United Kingdom (UK) and United Arab Emirates (UAE). The return spillover result exhibits that return of the conventional index of Japan and Brazil are negatively affected by changes in its sustainable index. The EGARCH results reveal the bidirectional volatility spillover in all the markets. The volatility shocks from the sustainable index of the United States of America (USA) and China have an asymmetric effect on the conventional indices of these countries. Further, positive asymmetric spillover was observed between the sustainable and conventional indices of Brazil and UK. The knowledge of linkage and asymmetric volatility spillover will assist the investors in portfolio management and formulating hedging strategies during financial and economic adversities.

Do Spot, Futures, and Options Markets Exhibit Price and Volatility Interdependence? Evidence from India

This paper analyses the price- and volatility-based interdependency among spot, futures, and options markets in a unified framework. The paper utilizes the vector error correction model generated under the TGARCH framework, the conventional pair-wise Granger causality test, the block exogeneity and vector Granger causality test, and Schwartz–Szakmary’s factor weights to decipher the price interdependence. The paper also uses the DCC-GARCH model to examine the existence of time-varying conditional correlation in volatility. The study finds evidence of the dependency among these three markets with a stronger leading role of spot against futures and options and significant positive/(negative) influence of the previous day’s spot/(futures) price change on options’ price change. Further, the asymmetric impact of price changes on conditional volatility is observed in the case of the spot and futures market. The results also exhibit the existence of time-varying conditional correlation among these markets with spillover effect.

Dynamic Volatility Connectedness among Cryptocurrencies: Evidence from Time-Frequency Connectedness Networks

This study examines the time-varying connectedness among the realized volatilities of seven major cryptocurrencies between January 2020 and May 2022. To this end, we implement the time and frequency connectedness time-varying parameter vector autoregression (TVP-VAR) approaches. Our findings propose that (i) the COVID-19 pandemic significantly affected the dynamic connectedness; (ii) the total connectedness index hits its apex around the official announcement of the pandemic; (iii) in line with previous studies Ethereum, Bitcoin, and Link are the largest propagators/recipients of shocks; (iv) the tightest volatility interdependencies are related to the short-run.

Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets.

This paper investigates (i) the return-volatility spillover between Bitcoin, Ethereum, Ripple, and Litecoin, (ii) the interdependence between cryptocurrencies’ volatility and the US equity and bond markets’ volatility, and (iii) the impact of the Covid-19 outbreak on the cryptocurrencies’ return-volatility. A two-step estimation approach is considered where Univariate General Autoregressive Conditional Heteroskedastic models are estimated to model the volatility of the four cryptocurrencies then a Simultaneous Equation Model is estimated to model the interconnection between the cryptocurrency volatilities, the US equity and bond markets’ volatility, and Covid-19 outbreak. We show that return-volatility spillovers exist among Bitcoin, Ethereum, and Litecoin while Ripple is the main transmitter of shocks. We find that the cryptocurrency market is detached from the US stock market but not from the US bond market. Finally, we show that a high economic and financial uncertainty in the US stock market due to pandemic outbreaks affects the price of Litecoin, Bitcoin, and Ethereum. However, shocks are short-lived. Our findings have practical implications; as the evidence of volatility spillovers among cryptocurrencies and their relative isolation from the majority of mainstream assets should be factored into the valuation and portfolio diversification strategies of investors. In crisis times such as those induced by Covid-19, investors who seek protection from downward movements in bond markets could benefit from taking a position in Ethereum. Policymakers can also rely on our findings to time their intervention to stabilize markets and control uncertainties inherent to stressful periods.

Volatility between Conventional and Islamic Stock Market

Purpose: This study investigates the degree and extent of volatility interdependence between the Islamic and conventional stock markets represented by using the Dow Jones indices for the period spanning from Dec 1, 2008, to Dec 31, 2016.
 Over the last two decades, the stock market has developed rapidly as a new investment instrument and these investments haven't solely extended within the money market and conventional market but also in the developing Islamic markets.
 Design/Methodology/Approach: GARCH(1,1) conditional volatility series are generated and then further used the Quantile regression approach on volatility series to check the financial markets’ interdependence.
 Findings: The results show that stock markets are interdependent on all three quantile levels. Accordingly, the structure of interdependence is asymmetric for both Islamic and conventional stock markets.
 Implications/Originality/Value: Investors, government regulators, and academics can all learn from the findings of this study. The potential for spillover of volatility from conventional stock markets to Islamic stock markets can be effectively countered by investors who develop and implement their unique strategies.

COVID-19 pandemic and volatility interdependence between gold and financial assets

ABSTRACT By using high-frequency data, we examine the volatility linkages patterns between gold and several important asset classes including foreign currency, US equity, oil, bitcoin and agriculture commodity in the period surrounding the COVID-19 pandemic. To this end, we use the cross-wavelet power transform, the cross-wavelet coherency and the dynamic frequency-domain connectedness. We find that the pandemic caused a greater positive association in volatility series between gold and each of the financial assets considered. We document clear findings of phase difference of lead-lag volatility interdependence between gold and the financial assets that varies according to timescales and periods. In general, the long-term connections are strengthened during the pandemic except the case of bitcoin and soya bean suggesting a long-term diversification ability when including them in a portfolio containing gold.

Agricultural commodity markets and oil prices: An analysis of the dynamic return and volatility connectedness

We investigate the joint and bivariate return and volatility interdependence between various agricultural commodities and oil price shocks. As an alternative of the Diebold and Yilmaz (2012 and 2014) spillover methodology, this paper proposes the application of the fresh time-varying parameter vector autoregression (TVP-VAR) methodology by Antonakakis and Gabauer (2017) during the sample period between January 7, 2000 and September 17, 2020. In addition, this paper pays special attention to the most relevant periods of economic turbulence among the last 20 years: dotcom bubble, Global Financial Crisis (GFC) and COVID-19 pandemic crisis. About the main results, the directional return and volatility connectedness of oil risk shocks is higher than oil demand shocks and, in turn, higher than oil supply shocks. In addition, the dynamic total return and volatility connectedness changes over time, rising during periods of economic crisis. In general, the net return connectedness considerably increases during the three most important crises. Thus, the differences between transmitters' (Canola and Corn) and receivers’ (Orange Juice, Lean Hog, Sugar and Rubber) agricultural commodity markets are emphasized during the GFC and the COVID-19 pandemic crisis. Finally, the net volatility connectedness measure would not show evidence as clear as the net return connectedness measure.

A Bayesian semiparametric vector Multiplicative Error Model

Interactions among multiple time series of positive random variables are crucial in diverse financial applications, from spillover effects to volatility interdependence. A popular model in this setting is the vector Multiplicative Error Model (vMEM) which poses a linear iterative structure on the dynamics of the conditional mean, perturbed by a multiplicative innovation term. A main limitation of vMEM is however its restrictive assumption on the distribution of the random innovation term. A Bayesian semiparametric approach that models the innovation vector as an infinite location-scale mixture of multidimensional kernels with support on the positive orthant is used to address this major shortcoming of vMEM. Computational complications arising from the constraints to the positive orthant are avoided through the formulation of a slice sampler on the parameter-extended unconstrained version of the model. The method is applied to simulated and real data and a flexible specification is obtained that outperforms the classical ones in terms of fitting and predictive power.

Stochastic asset flow equations: Interdependence of trend and volatility

The complex interaction between the volatility and the price trend is examined. We model stochastic asset prices using the asset flow model with randomness arising directly from supply and demand. We show that the volatility is smallest at the extrema of the expected logarithm of the price. Linearizing the stochastic differential equation (SDE) about equilibrium, we obtain an exact relation for the autocovariance function, and relate it to the (3 by 3) Jacobian of the linearized SDE. In particular, we find the conditions under which one has a pair of complex conjugate eigenvalues of the Jacobian resulting in oscillations. The frequency of the oscillations depends only on the imaginary part of the complex pair, while the decay rate depends only on the real eigenvalue and the real parts of the complex pair. For the deterministic system, oscillations typically decay rapidly. However, randomness induces oscillations to continue indefinitely with a frequency that depends on the parameters of the deterministic system. The computations and analytical results presented here demonstrate that volatility increases when traders place greater emphasis on trend, confirming a generally held belief among practitioners.

Cross-Time-Frequency Analysis of Volatility Interdependence among Stock and Currency Markets

Volatility transmission between stock markets and currency markets is an ongoing debate in the pertinent literature. However, the majority of the previous studies have used only daily data with a limited sample. This study aims to fill this gap by identifying how sample stock markets and currencies play the role of volatility transmitter and receiver, particularly on an intraday basis. To this end, this research detects volatility interdependencies among various stock markets and currencies using five major stock indices and six major currency pairs. The results for daily and intraday frequencies are quite disparate. In particular, the results signify that the transmission of volatility from currency markets to stock markets is much stronger on an intraday basis. The results also indicate a strengthening of the volatility transmission and spillover interdependence among stock markets on a daily basis. These results may be ascribed to the continuous trading mechanism of these markets, which in turn allows the news to impact these markets first, which then transmit it to stock markets. The findings obtained also imply that intraday price fluctuations in major currencies should be closely tracked to monitor intraday volatility patterns in stock markets.

Stochastic Asset Flow Equations: Interdependence of Trend and Volatility

The interaction between the volatility and price dynamics is explored. We model stochastic asset prices using the asset flow model with randomness arising directly from supply and demand. We show that the volatility is smallest at the extrema of the price. Linearizing the stochastic differential equation (SDE) about equilibrium, we obtain an exact relation for the autocovariance function, and relate it to the (3 by 3) Jacobian of the linearized SDE. In particular, we find the conditions under which one has a pair of complex conjugate eigenvalues of the Jacobian resulting in oscillations. The frequency of the oscillations depends only on the imaginary part of the complex pair, while the decay rate depends only on the real eigenvalue and the real parts of the complex pair. For the deterministic system, oscillations typically decay rapidly. However, randomness induces oscillations to continue indefinitely with a frequency that depends on the parameters of the deterministic system. The computations and analytical results presented here demonstrate that volatility increases when traders place greater emphasis on trend, confirming a generally held belief among practitioners.

DYNAMIC INTERDEPENDENCE BETWEEN CRUDE OIL PRICES AND FOREIGN EXCHANGE MARKET IN NIGERIA

Modelling volatility interdependence between crude oil and foreign exchange markets returns provides useful insights into how information is transmitted from the crude oil market to the foreign exchange market and vice versa. This paper evaluates dynamic interdependence between crude oil and foreign exchange markets by applying Baba, Engle, Kraft and Kroner (1990) (BEKK) specifications to crude oil prices and Naira/USD exchange rates. Estimates from the BEKK-GARCH (1,1) model indicate evidence of unidirectional shock and volatility transmission from crude oil market to foreign exchange market in Nigeria. Evidence of unidirectional volatility transmission provides support for partial interdependence between the markets in Nigeria. This finding has important implications for financial risk management, foreign exchange market regulation and crude oil revenue management policy.

Volatility interdependence on foreign exchange markets: The contribution of cross-rates

• The volatility interdependence of 21 bilateral exchange rates among 7 major currencies is examined. • We focus on the volatility interdependence between the USD (EUR) rate and its cross-rate. • The spillover index is estimated to identify the direction and magnitude of volatility spillovers. • Wavelet analysis is used to identify localized correlations of volatilities in time-frequency space. • We find significant contributions of cross-rates to the volatility interdependence in FX markets.

Is there an Interdependence in Foreign Exchange Markets During Non-Crisis Periods? Empirical Evidence from Mena Countries

The purpose of this article is to investigate the existence of volatility interdependence in different fixed-exchange-rates markets during non-crisis periods. Based on daily exchange rates from four Middle East and North African (MENA) countries (Saudi Arabia, Kuwait, Morocco, and Tunisia), we use an original approach, combining proper segmentation of our data sample with univariate and multivariate GARCH models. The main result is that fixed exchange rates do show different levels of volatility interdependence to the international market in both stable and crisis periods. Moreover, the type of exchange rate regime plays a significant role in maintaining the interdependence, while introducing flexibility reforms tend to reduce it and to increase the impacts of past shocks.

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic.

The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics

Carbon allowances are a new class of financial instrument which aim to assist in limiting the extent and impact of global warming and climate change. The feedback mechanism in the “Carbon-Energy-Finance” system makes the information connectedness dynamics more complex since we add equity, bond and non-energy commodity assets into the system. Using modified error variance decomposition and network diagrams, we quantify and systematically analyze how the European carbon market connects with information from a wide range of other markets. Our results indicate: (i) the nature of information spillover changes over time, with system-wide return connectedness being higher and more variable than the volatility interdependence; (ii) both the oil and carbon markets closely connect with equity and non-energy commodity markets rather than bond markets; (iii) we identify three structural breaks in carbon volatility and their implication for carbon-finance linkages; (iv) financial risk-type macroeconomic factors make greater contributions to system-wide connectedness than commodity factors. These findings have economic implications for investors, portfolio managers and policymakers. • Directional and dynamic connectedness in“Carbon-Energy-Finance”system is explored. • Network connectedness exhibits substantial time variation. • Structural breaks in Carbon return impact how it connects to the system. • System-wide spillovers are determined by financial risk-type macroeconomic factors. • EUA is a better diversifier than oil given its modest linkage with financial asset.

Another look at value and momentum: volatility spillovers

This paper examines volatility interdependencies between value and momentum returns. Using U.S. data over the period 1926–2015, we document persistent periods of low and high volatility spillovers between value and momentum strategies. Moreover, we find that the intensity of the volatility spillovers may change substantially in very short periods of time and that these shifts in spillover intensity can be linked to prominent economic events and financial market turmoil. Our results further demonstrate that value returns increase and momentum returns decrease monotonically with increasing volatility spillovers between the two strategies. Given this linkage between spillover intensity and returns, we propose a simple trading strategy which utilizes a volatility spillover index for allocating funds between value and momentum portfolios. The proposed trading strategy outperforms value and momentum strategies and generates payoffs that are not subject to option-like behavior.

THE INTER-RELATIONSHIP BETWEEN COMMODITY ENERGY PRICES AND STOCK MARKET VOLATILITY IN SAUDI-ARABIA

Background and Purpose: The present study examines the inter-relationship that exists between commodity energy price as well as stock market volatility in Saudi-Arabia. The focus of the study is to test if changes in commodities energy prices (oil related) cause significant changes in the stock market volatility of Saudi Arabia.
 
 Methodology: This study made use of a generalized autoregressive conditional heteroscedasticity model which has exogenous variables (GARCH-X), thus able to employ the commodity energy price inform of an exogenous so as to test the conditional variance of the Saudi-Arabia stock market return.
 
 Findings: The findings from the estimated model provide evidence that only the ARCH and GARCH parameters are significant while the exogenous variables are insignificant. It is concluded that other factors affect the volatility of the Saudi-Arabia stock market, but not the commodity energy price.
 
 Contributions: This study recommends that, policy makers, investors, and regulators should give emphasis on macro-economic variables and volatility interdependence with other correlated markets, especially during energy price shock that affected the volatility of Saudi-Arabia stock market. 
 
 Keywords: Energy price, GARCH-X, Saudi Arabia, stock market, volatility.
 
 Cite as: Alsufyani, M., & Sarmidi, T. (2020). The inter-relationship between commodity energy prices and stock market volatility in Saudi-Arabia. Journal of Nusantara Studies, 5(1), 270-293. http://dx.doi.org/10.24200/jonus.vol5iss1pp270-293

Dynamic asymmetric spillovers and volatility interdependence on China’s stock market

In this paper, we developed a novel measure to quantify the asymmetries in volatility spillovers, which emerge due to negative and positive shocks. Using high-frequency data of ten CSI 300 sector indices from 2007 to 2016, we employ our method to analyze the asymmetries in volatility spillover and volatility interdependence on China’s stock market at the sector level. We find that in general, the spillover from bad volatilities, which due to negative movements in returns, is considerably stronger than that from good volatilities due to positive returns. Except when the State Council of China announced the 4 trillion stimulus program in November 2008 and raised the total maximum QFII quota by $50 billion to $80 billion in April 2012, during which the good volatility dominated. Moreover, our empirical results reveal the asymmetric spillovers transmission mechanism. By decomposing the overall shock into macroeconomic shocks that affect all sectors and industrial shocks that only affect the specific sector, we found that rather than industrial shocks, the macroeconomic shocks dominate the asymmetries in spillover transmission.