Asymmetric Volatility Research Articles

Impact of central bank digital currency uncertainty on international financial markets

Is the impact of central bank digital currency (CBDC) uncertainty on investment and financial stability significant? To address this pivotal question in the era of CBDC development, we employ a combination of models: the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR); the Baba, Engle, Kraft, and Kroner multivariate generalized autoregressive conditional heteroskedasticity (BEKK-MGARCH) model, and wavelet quantile correlation (WQC) analysis. Based on the findings, macrofinancial variables significantly respond to CBDC uncertainty shocks, exhibiting time-varying, heterogeneous, and stage-specific characteristics. These effects are most pronounced in the short term, but diminish over the long term. Notably, the impact of CBDC uncertainty shocks on other financial variables exceeds the reverse impact. In addition, asymmetric volatility spillovers are found, suggesting complex interdependencies. The implication of the findings is that interactions between CBDC uncertainty and financial variables evolve across short-, medium-, and long-term horizons, emphasizing the need for central banks to adopt a flexible approach in CBDC design to alleviate market concerns and enhance overall financial stability.

Exploring Asymmetric Volatility Spillovers among Major Bombay Stock Exchange Indices

Exploring Asymmetric Volatility Spillovers among Major Bombay Stock Exchange Indices

Volatility spillover between carbon market and related markets in time-frequency domain based on BEKK-GARCH and complex network analysis

With rising public attention to climate issues and sustainable development, the connection between carbon market and its related markets has become closer. Based on BEKK-GARCH method and complex network theory, this study tries to explore the volatility spillover effects and network topology among China's carbon market, non-renewable energy market, renewable energy market, high-tech market and climate policy uncertainty index in time-frequency domain. It is found that, firstly, there are significant asymmetric volatility spillover effects among the markets. Secondly, the spillover effects between markets are time-varying, especially during crisis periods in which they are significantly higher than that during the stable period. Thirdly, with time increasing, the closer the connection between markets. In the long run, the closeness of the connection is directly proportional to the spillover effect. Finally, climate policy uncertainty is the main risk source. For full sample, from perspective of frequency domain, non-renewable energy and carbon markets are the main risk recipients. Based on the findings, it should step up efforts on market monitoring and intervention during crisis periods, strengthen cross-market risk management, pay attention to climate uncertainty and time-frequency varying characteristics of volatility spillovers, so as to provide useful information for policy makers and investors.

COVID-19 and time-frequency spillovers between oil and sectoral stocks and portfolio implications: Evidence from China and US economies

This paper examines the volatility spillovers and the time-frequency dependence between crude oil and stock sectors in the U.S. and China using both wavelet coherence and asymmetric BEKK GARCH models. This study also investigates the impact of the COVID-19 pandemic on spillover effects and portfolio management. The results reveal strong positive co-movements between WTI oil and US sector stock returns at medium and low frequencies particularly in 2020Q1. We find significant long-term co-movements between oil and Chinese sector stock returns (64–128 days). Furthermore, the findings reveal a significant and asymmetric volatility transmission between oil and sector stocks in both the US and Chinese economies. The evidence of spillovers is more pronounced during the COVID-19 period. It is recommended that equity investors should hold more stocks than oil assets to minimize risk without reducing the expected return. Finally, hedging with oil is expensive for U.S. sectors during the pandemic but inexpensive for Chinese sectors.

Price limit bands, risk-return trade-offand asymmetric volatility: Evidence fromTunisian Stock Exchange sectors

This paper explores the impact of imposing various price limit bands on risk-return trade-off and asymmetric volatility on the Tunisian Stock Exchange (TSE). The study applies the EGARCH-M approach during the period spanning from 2 January 2019 to 31 January 2024, covering the periods before, during, and after the COVID-19 era. During the COVID-19 period, the TSE reduced the per-session price limit to protect investors from severe price fluctuations. Despite this protective measure, the results show that higher volatility is compensated by lower returns on all sectors’ returns. After the crisis, as a first step, the TSE widened the price limits, but subsequently, it narrowed them. The results show that the shift from the wider price limit regime to the narrow price limits regime structurally modifies volatility for small and large cap sectors.

Modelling Asymmetric Volatility in the Crypto Currency and its Dynamic Relationship with Stock Market

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

Electricity and cryptocurrency mining: An empirical contribution

Active cryptocurrency mining and trading comes with heavy electricity demand and increased emissions. Thus, cryptocurrency mining is prohibited in most economies. Consequently, miners relocate to regions or economies without these prohibitions and/or with relatively lower electricity rates. As such, presenting a nexus between the cryptocurrency and electricity markets, even at the global level. This article investigates the different forms of relationships existing between these markets. The conditional asymmetric volatility model with the Wald, nonparametric and parametric Granger causality tests are employed. The results confirm the existence of both unidirectional and bidirectional lead-lag return relationships between the cryptocurrency and electricity markets. Cryptocurrency returns drive electricity demand. This finding is homogeneous both on a global and strata (homogeneous groupings) basis. Also, the electricity market spills over significant volatilities to the cryptocurrency markets without feedback, nonetheless. Result-based policies are recommended towards green finance, decarbonization, and emission mitigations through the demand for electricity by the cryptocurrency markets. They include the use of clean and renewable electricity sources and technologies for cryptocurrency market activities.

Context-dependent responses to geopolitical risk in Middle Eastern and African stock markets: An asymmetric volatility spillover study

This paper examines the asymmetric volatility spillover impact of geopolitical risk on stock market returns across six Middle Eastern and African (MEA) economies: Egypt, Israel, Saudi Arabia, Tunisia, Turkey, and South Africa. Using the asymmetric MGARCH (Multivariate GARCH) model of BEKK (Baba, Engle, Kraft, and Kroner), the study identifies different responses to geopolitical risk (GPR) index in these markets and its division into act- and threat-related components. The results show that the susceptibility and responses to these risks are highly context-dependent, reflecting the unique economic and geopolitical structures of each market. Moreover, these economies exhibit an asymmetric response to geopolitical risk, highlighting the importance of not only the magnitude but also the direction of these risks. The study underscores the need for investors to adopt a nuanced view of geopolitical risk and its multifaceted impacts on different markets. These findings hold valuable implications for investment decisions and policy-making in these economies.

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Modeling the News Impact on Volatility to Refugees Movement from LMICs: An APARCH Approach

Political and economic instability, armed conflicts, and weather abnormalities may introduce a high degree of volatility to the refugee movement. Refugee movement volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it differently. APARCH is one of the GARCH extension types of models that are popularly used to capture asymmetric volatility. In this paper, an attempt is made to model the volatility of the yearly refugee movement for the low-and middle-income countries using the APARCH model. The key finding of this study is that GARCH-extension type models indicating past volatility of the annual number of refugees is significant, influencing current volatility. The News Impact Curve is derived from the fitted APARCH model, which confirmed the presence of asymmetry in the volatility of the refugee movement. The News Impact Curve of the models for both groups of countries described that negative shocks of the same magnitude have a larger impact on volatility to refugee movement than positive shocks.

Does Volatility Spillover among Sectors Varies from Normal to Turbulent Periods? Evidence from Pakistan Stock Exchange

This study investigates how six financial market performances are affected by spillover volatility sectors in both during and following the 2007 financial crisis in Pakistan. By offering a wealth of material to fill in the gaps during the 2007 pre- and post-financial crisis timeframe, this study adds to the body of current knowledge. The pre-crisis era, which ran from January 1, 2003, to December 31, 2007, was used for the analysis on the Pakistani sectors. The period from January 1, 2008, to December 31, 2015, was known as the post-crisis era. The Pakistan Stock Exchange and other sources provided the data, which was routinely gathered. An ARCH, GARCH & EGARCH model is used to investigate the asymmetric volatility spillover among the six industries. The results provide compelling evidence for the existence of a spillover impact in Pakistani businesses in the years after the crisis. Furthermore, there is a significant transmission of volatility throughout Pakistan's many industries during the financial crisis. Overall, the results provide insight into the varying degrees of integration between sectors in Pakistan, ranging from stable to volatile eras. The results indicate that no significant vulnerability was detected. Additional elements can be included and the timing of data can be adjusted. In this study, data is currently gathered monthly. However, in the future, data might be collected on a daily, weekly, or yearly basis to enhance the reliability of the data and its corresponding conclusions. This study expands the existing body of research on volatility and spillover effects in several industries of Pakistan. This research is an innovative study being conducted in six market sectors. The primary impetus for conducting this inquiry is the financial catastrophe in the United States, which has had repercussions on global markets as a whole. Moreover, this time frame is also utilized to inspect the correlation in between financial crises and stock market returns in Asian emerging markets.

Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets

This study includes tests on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its derivatives to conduct complex and detailed volatility analysis for the 5 highest-volume cryptocurrencies traded in September 2023. The tests have been conducted with Python, R, and Eviews software and analyses have been compared in terms of consistency and accuracy of the results across multiple software and programming languagse. In the testing process, observation of the volatility has been assessed by some variables such as skewness, kurtosis, and log-likelihood values, and these variables have been taken into consideration for testing. Tests such as Jarque-Bera and Augmented Dickey-Fuller (ADF) have been applied during the process to verify model correctness. The EGARCH, GJR-GARCH, and TGARCH models have been more effective in detecting volatility and market shocks in the relevant cryptocurrencies as a result of the tests conducted in the volatility analysis.

Calendar anomalies and asymmetric volatility of returns in the Indonesian stock market: conventional vs Islamic indices

Purpose This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices. Design/methodology/approach The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022. Findings The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices. Practical implications This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency. Originality/value The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia)

Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.

Decoding herding dynamics in the generative AI investment amid key technological advancements: A timeline perspective

This study, using two herding dynamics metrics and Glosten–Jagannathan–Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model, forecasts market trends, captures asymmetric volatility, and reveals the generative AI (GenAI) ecosystem's impact on individual assets’ returns. Results of this study highlight distinctive traits of each GenAI equity, crucial for strategic positioning, especially for investors in tech stocks tied to GenAI. Herding behavior exhibits greater strength in the initial four months post-announcement of ChatGPT, gradually diminishing. GJR-GARCH reports that most of GenAI stocks do not exhibit statistically significant leverage effects. These findings provide valuable insights to navigate the dynamic landscape of GenAI investments.

Return and Volatility Spillover in Equity and Commodity Market: Some Indian Evidence

This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns’ causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43

Asymmetric Volatility in Stock Market: Evidence from Selected Export-based Countries

In a country, the financial markets depend on several economic factors. One of these important factors is the export of the country. The stock index of the country tends to be raised due to the exports, though it may occur after some time rather than immediately. In this study, an attempt has been made to predict the best model of volatility in top export countries, Germany, China, the United States, Japan and Hong Kong by taking into account the closing price of the stock index of the sampled countries for a period ranging from the year 2000 to 2020. The returns from the stock markets are asymmetric; negative returns are found to be followed by a greater increase in volatility than the corresponding positive returns. Therefore, both symmetric and asymmetric generalised autoregressive conditional heteroscedasticity (GARCH) models have been applied to predict the volatility. The symmetric model used is GARCH (1,1) and asymmetric models used in the study are exponential GARCH (1,1) and GJR-GARCH (1,1). The study shows that EGARCH model has outperformed the GARCH and GJR-GARCH models in estimating the volatility in four stock indices ( Hanif & Pok, 2010 ; Kışınbay, 2010 ; Lin, 2018 ), and GJR-GARCH has outperformed in estimating volatility in one stock index ( Oberholzer & Venter, 2015 ; Shamiri & Hassan, 2007 ). The benefit of this study is to help portfolio managers, investors and corporations in making investment-related decisions. JEL Codes: C20, C31, C58, G12

Testing event-based day of the week anomaly and trading opportunities: Evidence from Indian sectoral indices

The study is an attempt to examine the day-of-the-week anomaly of fourteen Indian sectoral indices and identify profitable opportunities, considering multiple positive and negative events. The aim of this study is to analyze the day-of-the-week effect on fourteen Indian sectoral indices and find profitable opportunities while considering multiple events that have positive and negative impacts. The study takes into consideration event-based anomalies, both national and global, and provides timing for trading to generate abnormal returns from the market. At first, dummy variable regression analysis was used to understand the initial anomalies. Later, time-varying symmetrical and asymmetrical volatility models, such as Generalized Autoregressive Conditional Heteroscedasticity (1, 1) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (1, 1) were applied to determine the short-term and long-term volatility persistence. These models capture the leverage effect from various events that occurred during the study. The results showed mixed outcomes during multiple positive and negative shocks. After the recession, anomalies were observed across all sectoral indices, except for commodities, energy, and information technology. During the scam period, anomalies occurred in all sectors, except for consumer durables, financial services, and information technology. However, after the new government took over, anomalies persisted in all sectors. During the pandemic, anomalies persisted in all sectors except for finance, IT, pharmaceuticals, and services. Hence, national and global events have shown varied impacts on the Indian markets. The study provides investors with implications on strategies and timing techniques for planning their investments in different sectors of the Indian economy.

Asymmetric Volatility Connectedness Among G7 Stock Markets

Asymmetric Volatility Connectedness Among G7 Stock Markets

중국 주택시장의 비대칭적 변동성에 관한 연구

Purpose – This study analyzes asymmetrical volatility in the Chinese housing market. Particularly, it aims to find whether housing market returns are positively or negatively related to subsequent volatility. Design/Methodology/Approach – The GJR-GARCH model applies to returns on the Chinese housing price index from January 2006 to December 2022. To consider the possibility that the nature of asymmetry depends on the sample period, we repeatedly estimate multiple sub-samples by increasing the end point by one point with the starting point of the sample fixed at January 2006. The period of January 2009 - December 2022 was also analyzed, using January 2009 as the starting point, to exclude the impact of large negative returns during the 2008 global financial crisis. Findings – First, t he nature of a symmetry varies g reatly depending on t he e nd point. Second, when January 2006 is the starting point, a negative relationship between returns and subsequent volatility referred to as typical asymmetry is found for most end points between December 2009 and May 2011, which seems closely related to large negative returns during the 2008 crisis. Third, when January 2009 is the starting point, a positive relationship between returns and subsequent volatility called reverse asymmetry is revealed when the end point is between December 2012 and November 2013, and between August 2016 and December 2022. Research Implications – The reverse asymmetric volatility for the period of 2009-2022 points to the possibility of speculative bubbles in the housing market, supported by the crisis in the Chinese real estate industry from 2021 to 2023.