Asymmetric Volatility Research Articles

Asymmetric volatility of the US dollar index in the stock and cryptocurrency market during the Russia-Ukraine war

Asymmetric volatility of the US dollar index in the stock and cryptocurrency market during the Russia-Ukraine war

Asymmetric volatility spillover between oil prices and regional renewable energy stock markets: A time-varying parameter vector autoregressive-based connectedness approach

<abstract> <p>The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into <italic>good</italic> and <italic>bad</italic> volatility emphasizes an asymmetric pattern, which becomes more pronounced during periods of major events. On average, the oil market and the Asian renewable energy stock market are net receivers of volatility shocks. In contrast, the US and European renewable energy stock markets are net transmitters of shocks. Our findings provide investors with valuable insights for portfolio design and risk management decisions.</p> </abstract>

Asymmetric volatility impulse response functions

Asymmetric volatility impulse response functions

Global Uncertainty and Exchange Rate Volatility

This paper investigates the impact of global uncertainty on Turkey's exchange rate volatility via quantile regression approach. Using quantile regression approach, estimated uncertainty coefficients are allowed to differ over quantiles of the exchange rate volatility. The EGARCH model is the best fit for measuring exchange rate volatility due to the fact that exchange rate series exhibit “asymmetric volatility”. In this study we employed global economic policy uncertainty indexGEPU constructed by Baker et al. (2013) as a proxy of global uncertainty. Empirical results suggest that higher volatility of exchange rate is associated with a greater positive shock of GEPU. However, estimated parameters are statistically significant at lower exchange rate volatility since the CBRT intervenes the foreign exchange markets and restricts the excessive fluctuations in exchange rates to achieve financial stability.

Modeling asymmetric volatility of financial assets using univariate GARCH models: An Indian perspective

In recent years, numerous models with various amounts of variance have been developed to estimate and forecast important characteristics of time series data. While there are many studies on asymmetric volatility and accuracy testing of univariate Generalized Autoregressive Conditional Heteroscedasticity models, there are no parallel studies involving multiple financial assets and different heteroscedastic models and density functions. The objective of this study is to contrast the forecasting accuracy of univariate volatility models with Normal and Student-t distributions in forecasting the volatility of stock, gold futures, crude futures, exchange rate, and bond yield over a 10-year time span from January 2010 through December 2021 in Indian market. The results of exponential, threshold and asymmetric power models show that the volatility stock (–0.12047, 0.17433, 0.74020 for Nifty, and –0.1153, 0.1676, 0.7372 for Sensex), exchange rate (–0.0567, 0.0961,0.9004), crude oil futures (-0.0411, 0.0658, 0.2130), and bond yield (–0.0193, 0.0514 and –0.0663) react asymmetrically to good and bad news. In case of gold futures, an inverse asymmetric effect (0.0537, –0.01217, –0.1898) is discovered; positive news creates higher variance in gold futures than bad news. The Exponential model captures the asymmetric volatility effect in all asset classes better than any other asymmetric models. This opens the door for many studies in Indian financial market.

Does the asymmetric dependence volatility affect risk spillovers between the crude oil market and BRICS stock markets?

Earlier studies have confirmed the asymmetry as a key feature of return volatility and risk spillovers. However, little research has explored the impact of asymmetric dependence volatility on risk spillovers. In this paper, we propose the asymmetric generalized autoregressive score-time-varying mixture model to analyze this issue. Using a dataset covering the crude oil market and BRICS stock markets from January 11, 2000 to June 11, 2021, we find the tail dependence of these markets is falling faster than it is rising, and this feature is more significant in upper tail dependence. Also, the capture of asymmetric dependence volatility is beneficial for the estimation of risk spillovers. Moreover, the risk from the crude oil market will amplify the downside risks in BRICS stock markets. In addition, downside risk spillovers between these markets are significant during the COVID-19 pandemic. • A new approach is proposed to capture asymmetric dependence volatility between the crude oil market and BRICS stock markets. • The tail dependence of these markets is falling faster than it is rising. • The asymmetric volatility is more significant in upper tail dependence. • The capture of asymmetric dependence volatility is beneficial for the estimation of risk spillovers. • Downside risk spillovers are significant during the COVID-19 pandemic.

Examining Asymmetric Volatility Dynamism of Returns in the Infrastructure Sector in India during Covid 19: - A application of GARCH Models

Due to global shut down of economic activities and transportation, the infrastructure sector has to see a halt in operations due to disruptions in supply chain, impacting international investors as they became cautious of their investment position. The study is aimed at modelling the volatility of the returns in the infrastructure sector in India using S&P BSE Infrastructure Index during Covid-19 by applying univariate stipulations of the GARCH family of models such as GARCH (1,1), EGARCH (1,1), MGARCH (1,1). The study found the presence of asymmetric effects indicating that the arrival of Covid 19 news created more turmoil in the market. Also, significant relationship has been observed between the magnitude of variance and returns, meaning thereby the global investors, while making portfolio decisions, should emphasize that high risk implies high returns holds true for Infrastructure sector in India. The study suggests that the investors, while estimating value at risk, should consider that the infrastructure returns depict higher persistence level and the past volatility of the returns in the infrastructure sector has a significant impact on current volatility in case of BRICS economies.

EMPIRICAL ANALYSIS OF THE VOLATILITY EFFECT OF THE COVID-19 PANDEMIC PROCESS ON NATURAL GAS FUTURE TRANSACTIONS IN TURKEY

The aim of this study is to investigate the volatility movements in natural gas returns, which is one of the financial investment instruments in futures markets, before and after the Covid-19 pandemic, using GARCH family models. For this purpose, daily data from 30.08.2017 to 10.03.2020 before the Covid-19 Pandemic, and daily data from 11.03.2020 to 21.09.2021 after the Covid-19 Pandemic were used. The return on natural gas futures before the Covid-19 Pandemic was expressed as RLNPO and the return on natural gas futures after the Covid-19 Pandemic was expressed as RLNPS. For RLNPO, TGARCH was determined as the most suitable volatility model according to Schwarz Information Criteria, and EGARCH was determined as the most suitable volatility model for RLNPS. As a result of these analyzes, it has been seen that natural gas futures returns can be explained by asymmetric volatility models before and after the Covid-19 Pandemic, but there is no leverage effect as a result of asymmetric volatility, and positive shock asymmetries have a greater effect on volatility. The asymmetric effect tends to decrease in the post-Covid-19 Pandemic period.

Improved parameter estimators for the flexible extended skew-t model with extensive simulations, applications and volatility modeling

This study introduces a new conditional innovation density called the generalized odd generalized exponentiated skew-t (GOGEST) distribution for the generalized autoregressive conditional heteroscedasticity (GARCH) volatility models. Structural properties of the GOGEST distribution such as the density and cumulative functions, failure rate and quantile functions are presented in explicit forms. The simulation study indicates that the best estimator for estimating the GOGEST parameters is the maximum likelihood procedure. Applications to two real skewed datasets established that the GOGEST distribution provides the best fit compared with existing distributions. Moreso, new GARCH-type models with GOGEST distributed innovations for time series showing asymmetric volatility is proposed. An empirical application to a real dataset concerning the First bank Nigeria (FBN) daily shock return is considered to demonstrate the superiority of the proposed GARCH-type models specified with GOGEST density relative to the skew-normal, skew-t, skew generalized error, generalized hyperbolic, Johnson reparametrized, normal, Student-t, generalized error, generalized hyperbolic skew Student-t and Normal inverse Gaussian. Overall, the empirical findings validate that the Glosten-Jagannathan-Runkle GARCH (GJRGARCH)-GOGEST model provides better in-and out-of-sample predictive performance compared with other models. Therefore, the GOGEST density should be an extensively useful conditional innovation density in GARCH-type specifications for modeling volatility.

Is Tether a safe haven of safe haven amid COVID-19? An assessment against Bitcoin and oil using improved measures of risk

Bitcoin is a new speculative investment with extremely volatile movement, thus possibly failing to act as a safe haven for crude oil when the price of this energy commodity plummeted following the global outbreak of COVID-19. Meanwhile, Tether is designed to behave similarly to the US dollar with stable fluctuation. In this study, we assessed their safe-haven properties in terms of risk reduction opportunities by proposing an improved version of Value-at-Risk (VaR) and Expected Shortfall (ES). Using vine copula-based AR-GJR-GARCH models, we demonstrated that Bitcoin exhibited inconsistent risk reduction capability for oil, particularly before COVID-19. When adding Tether into a portfolio containing oil and Bitcoin, the risk reduction was achieved for any portfolio allocation and was more pronounced amid the COVID-19 period. This suggests that Tether consistently served strong support for Bitcoin to protect oil investors against extreme risk and received a significant impact from the COVID-19 outbreak. However, the consistent safe-haven functionality of Tether was not as good as that of the US dollar in most cases, and this implied the vanishing of its stability. These results were robust when considering another asymmetric volatility model and another dependence model. Furthermore, the proposed improved VaR and ES forecasts outperformed their corresponding unimproved version in quantifying portfolio risk and therefore provided a more accurate assessment of safe-haven roles.

Asymmetric volatility spillover between hospitality sub-sectors during COVID-19: evidence from the USA

PurposeThis article examines dynamic volatility spillovers between stock index returns of four main hospitality sub-sectors in US during the coronavirus disease 2019 (COVID-19) pandemic. These are tourism and travel, hotel and lodging, recreational services and food and beverages. Volatility spillovers are explicitly used as accurate and informative proxies for risk contagion between sectors during turbulent times.Design/methodology/approachThe authors employ dynamic conditional correlation-generalized autoregression heteroskedasticity (DCC-GARCH) and wavelet coherence analysis (WCA) to analyze the phenomenon. The authors’ timeframe is divided into three main sub-periods, namely the pre-pandemic, the first wave and the second wave periods.FindingsThis study’s results reveal immense negative shocks in returns of all four sub-sectors on the Black Monday (8th March 2020). Moreover, high volatility persistence was observed during both waves with an exception of tourism and travel which exhibited lower volatility persistence during the second wave. The authors discovered magnified contagion effects between tourism and travel, hotel and lodgment and recreational services during the first wave of the pandemic with tourism and travel being the main volatility transmitter. Lower magnitudes of spillovers were observed between food and beverages and other sub-sectors with a decoupling effect being evident during the second wave.Research limitations/implicationsThis study’s findings contribute to the contagion theory by providing evidence of disproportional volatility spillover among hospitality sub-sectors despite being exposed to similar turbulent economic conditions.Practical implicationsCrucial implications can be drawn from this study’s findings to assist in risk management, asset valuation and portfolio management. The importance of close monitoring, safety measures, international diversification and adequacy of liquid assets during health crises cannot be stresses enough for hospitality firms. Retail investors, speculators and asset managers can take advantage of this study’s findings to design trading strategies and hedge against risk.Originality/valueA body of knowledge pertaining to effects of crises such as COVID-19 on hospitality stocks has been proliferating. Nonetheless, there is still a relative dearth of empirical literature on volatility spillover between hospitality sub-sectors especially during periods of rising economic uncertainties.

Asymmetric Volatility Bias and Loss Aversion in Investment Decision-Making

The goal of the research is to investigate if the investor considers the volatility as asymmetric. The researcher as called this behaviour asymmetric volatility bias, because in the mind of the investor the volatility is associated only to the risk, without consider the potential benefit of the volatility during an upward trending market. The research aims to investigate if the asymmetric volatility bias is a confirmation of the existence of the loss aversion bias. Keywords: Asymmetric volatility bias, volatility, loss aversion, prospect theory.

The Irrational Behaviour of Using Risk Control with Momentum Strategy During a Bullish Market Trend, an Example of Asymmetric Volatility Bias and Loss Aversion

The goal of the research is to investigate if the application of the risk control to the momentum strategy is a proof that the investor has asymmetric volatility bias that led a behavioural tendency to associate the volatility only as a risk factor, without consider potential upside effect of the volatility, the researcher as called this behaviour asymmetric volatility bias. The risk control apply to the momentum during a bullish market is an example of loss aversion and asymmetric volatility bias. Keywords: momentum strategy, risk control, volatility, herd behavior, loss aversion, disposition effect, asymmetric volatility bias, irrational behaviour and recency bias.

Cryptoasset Returns: Statistical Properties and Implications for Asset Allocations

We examine Bitcoin returns’ statistical properties and check whether these properties are consistent with the well-known stylized facts of asset returns for the period from January 2019 to May 2022. We find that Bitcoin exhibits price behaviors that correspond well to all the stylized facts of asset returns examined in the analyses. We also examine the returns of Tesla stock, NASDAQ index, and S&P500 index for comparison and find that they generally conform to the stylized facts examined. However, Tesla stock does not exhibit an asymmetric volatility feature, and the two indices show significant autocorrelations in low order time lags, which are inconsistent with the well-known stylized facts of asset returns. Based on these findings, we discuss the possible implications of cryptoassets for asset allocation processes. Cryptoassets provide some potential utilities for asset allocations, but only with several simultaneous limitations.

International Information Spillovers and Asymmetric Volatility in South Asian Stock Markets

This is the first comprehensive study to investigate the dynamics of international information spillovers, regional linkages and fundamental forces driving return volatility in the SAARC (South Asian Association for Regional Cooperation) member nation equity markets. We propose a multi-factor model nested within the generalized autoregressive conditional heteroskedasticity framework and enlist comprehensive equity market data. While modeling, we consider global, regional (Asia), and largest neighboring (India) equity markets as sources of information spillover. Our results show that equity returns in all these South Asian markets have positive autocorrelation. The equity markets of India, Pakistan, and Sri Lanka have some degree of global integration; however, their degree of regional integration is comparatively higher. The stock markets of Bangladesh and Nepal, in contrast, lack both global and regional integration. We find limited evidence of neighborhood (India) spillover effect on other markets in the sample. The stock markets of Bangladesh, India and Pakistan stock markets exhibit asymmetric volatility responses, while Nepal exhibits an inverted asymmetric volatility response, and in contrast Sri Lanka exhibits a symmetric volatility response to return shocks. Finally, most of these markets experience volatility spillover effects from the US, Asia, and India stock markets.

Volatility spillovers from the Chinese stock market to the U.S. stock market: The role of the COVID-19 pandemic

The COVID-19 pandemic, which originated in Wuhan, China, precipitated the stock market crash of March 2020. According to published global data, the U.S. has been most affected by the tragedy throughout this outbreak. Understanding the degree of integration between the financial systems of the world's two largest economies, particularly during the COVID-19 pandemic, necessitates thorough research of the risk transmission from China's stock market to the U.S. stock market. This study examines the volatility transmission from the Chinese to the U.S. stock market from January 2001 to October 2020. We employ a variant form of the EGARCH (1,1) model with long-term control over the excessive volatility breakpoints identified by the ICSS algorithm. Since 2004, empirical evidence indicates that the volatility shocks of the Chinese stock market have frequently and negatively affected the volatility of the U.S. stock market. Most importantly, we explore that the COVID-19 pandemic vigorously and positively promoted the volatility infection from the Chinese equity market to the U.S. equity market in March 2020. This precious evidence endorses the asymmetric volatility transmission from the Chinese to the U.S. stock market when COVID-19 broke out. These experimental results provide profound insight into the risk contagion between the U.S. and China stock markets. They are also essential for securities investors to minimize portfolio risk. Furthermore, this paper suggests that globalization has carefully driven the integration of China's stock market with the international equity markets.

Jump Dynamics and Leverage Effect: Evidences from Energy Exchange Traded Fund (ETFs)

Abstract This paper is concerned with the behavior of energy ETF prices. It applies three models: autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH), along with their revised forms, ARMA–Exponential-GARCH, Glosten-Jagannathan-Runkle (GJR), and GARCH diffusion process with jump models. This study looks at the volatility behavior and jumps dynamics of Energy and Master Limited Partnership's (MLP) ETFs. The results show that ARMA-GARCH is appropriate for modeling energy and MLP ETFs. Both ETFs offer positive leverage and asymmetric volatility. The results show that the jump model with a GARCH volatility specification has an actual amount of jump presence and time variation in the jump size distribution. The conclusion of the ARMA - EGARCH model gives evidence of the reverse leverage effect. The leverage term positively influences the conditional variance, while the asymmetry coefficient for the GJR model is positive and significant. These results reveal that both Energy and MLPs ETF have high volatility. JEL classification numbers: F3. Keywords: Energy ETFs, MLPs, ARMA-GARCH model, Volatility Asymmetry, Leverage and Jump Effect.

Impact of COVID-19 on the Performance of Indian Stock Market: An Empirical Analysis

The outbreak of COVID-19 epidemic had not only brought destruction to the human lives but also destabilized the financial markets across the world. Although there were some studies conducted on the impact of COVID-19 on the financial markets in the developed economies, very few studies were conducted on the developing economies like India. Hence, this study intends to measure the impact of COVID-19 on the Indian stock market, especially on NIFTY50, and all the major sectorial indices of National Stock Exchange (NSE). The study also makes an attempt to analyze the impact of COVID-19 on the Indian stock market in various time periods (lockdown, pre-lockdown, and full sample time periods). For this purpose, the researchers have used EGARCH and regression models to measure the sectoral impact of COVID-19 on NIFTY. The study finds asymmetrical reactions on positive and negative shocks in the Indian stock market. The β2 coefficient, which explains asymmetric volatility, is significant and positive for FMCG, realty, oil and gas, and consumer durables, suggesting the presence of asymmetric effect, but has no leverage effect. It implies that positive news has greater effects on volatility than negative news. In other words, investors are more prone to positive shocks than negative shocks with the same magnitude. While β2 is found to be significant and negative for NIFTY, bank, information technology, and financial services, which clearly depicts the presence of leverage effect. It suggests the presence of asymmetrical reactions on unfavorable shocks in these indices.

Asymmetric Price Volatility Transmission in Agricultural Supply Chains: Evidence from the Chinese Pork Market

The asymmetric price volatility transmission issue in agricultural supply chains has been ignored in the previous literature. This paper applies an asymmetrical MGARCH-BEKK model to investigate the asymmetric price volatility transmission in agricultural supply chains with an application to the Chinese pork market. Additionally, we use the Zivot–Andrews unit root test with a structural break to examine whether the piglet, hog, and pork prices have structural breaks. The results show that pork’s market prices have a structural breakpoint in 2007M03 and support the existence of the asymmetric volatility transmission in Chinese pork supply chains. Furthermore, the volatility spillover effects are different before and after 2007M03.

Understanding leveraged ETFs’ compounding effect

PurposePrior literature has shown that, theoretically, holding-period returns of a leveraged exchange-traded fund (LETF) are generally negatively affected by the volatility of the underlying benchmark’s daily returns, particularly for long holding periods. However, recent empirical studies simulate LETFs’ returns using historical benchmark returns and report results that are not entirely consistent with the theoretical predictions, leading to the possibility that the distribution of real-world returns may have certain characteristics that influence the outcomes. In this paper, the authors examine how asymmetric volatility affects LETFs’ performance and provide detailed explanations for the behavior of the performance of LETFs under different market conditions.Design/methodology/approachThe authors conduct simulation analyses on a +3x LETF and a −3x LETF based on historical S&P 500 stock index returns, with asymmetric volatility incorporated into the model.FindingsBy incorporating the asymmetric volatility effect, the simulation results suggest that, contrary to the theoretical predictions, higher volatility does not always lead to more negative impact on LETFs’ performance. Rather, the performance depends on the market conditions under which high volatility occurs. The findings therefore help reconcile prior theoretical predictions with reported empirical findings.Originality/valueThe analysis adds to the literature by incorporating the asymmetric volatility effect of stock returns in studying LETFs’ performance. The authors also provide detailed explanations for the behavior of LETFs’ returns and compounding effect under different market conditions, thus providing contexts to prior empirical results.