Volatility Persistence Research Articles

PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.

Research on the volatility forecasting model of KOSPI index returns using AR(M)-GARCH(P,Q) model

In this study, we estimated the volatility of the KOSPI index returns and analyzed volatility trends. The data used in the study consisted of monthly observations from January 2005 to December 2022, and the KOSPI index raw data was transformed into log returns. The volatility estimation model used the AR(m)-GARCH(p,q) model, which combines the autoregressive error model and the GARCH model that explains the persistence of volatility at low orders. The goodness of fit of the model was confirmed using the Portmanteau Q-test and LM-test. Applying the autoregressive error model revealed significant autocorrelation in the log returns of the KOSPI index at lags 3 and 6. Residual analysis indicated that the residuals followed white noise, but the squared residuals exhibited heteroscedasticity. Therefore, after fitting the autoregressive error model, we applied the GARCH model and conducted residual analysis, finding both the residuals and squared residuals significant at a 5% significance level. The volatility forecasting results indicated a continuous increase in volatility. The findings of this study are expected to provide important implications for policymakers responsible for risk management in the Korean stock market.

Safe Havens Characteristics of Islamic Stock Indices During Bearish and Bullish Markets: A Case of Developed and Developing Countries

Purpose — This study examines the performance of Islamic and conventional indices during bullish (positive) and bearish (negative) markets while the economic and financial conditions are different. Design/Methodology/Approach — This study uses GARCH (1,1) and wavelet-assisted cross-spectral, cross-correlation analyses to investigate the performance of Islamic and conventional stock indices and study the presence of safe havens, which reflect low volatility in the indices during times of market turbulence, for investors during bearish and bullish periods from 2019–2021. Findings — The paper finds that there is high and persistent volatility in Islamic indices in bullish markets, while in bearish periods the Islamic indices show low volatility persistence; and conventional indices were highly volatile during crisis periods. Also, the impact of news shocks on Islamic indices was intense before COVID-19, while after that news, shocks had a high impact on the conventional indices. The wavelet coherence analysis shows that there is high coherency between Islamic and conventional indices in developed countries but in developing countries, the opposite prevails. Originality/Value — The study is the first to use both Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and wavelet coherence models for examining Islamic and conventional stock indices during bearish and bullish markets, especially in the COVID-19 pandemic era. Practical Implications — The results conclude that there are safe havens present in Islamic indices in both developing and developed countries, especially the United States (US), the United Kingdom (UK), India, and Indonesia, while in other countries the presence is weak. Both GARCH and wavelet coherence models show the same results regarding the presence of volatility and safe havens for investors in different countries.

Investment volatility during red sea crisis: Study in Asean country

The objective of this research is to investigate the volatility of investment instruments, with the aim of providing the best model for predicting investment value. In this topic, the main concepts and theories that underpin the research are presented. Efficient market hypothesis and rational expectations theory stand out, providing a solid basis for understanding the context of the investigation. The methodology adopted for this research GARCH modelling to analysis investment instrument and the impact of its volatility. Data collection was carried out through yahoo finance website and investing website during red sea war event. The research identified optimal GARCH models for different indexes and assets: GARCH (1,1) for JCI, STI, SET, and Bitcoin, E-GARCH (1,1) for KLSE, PSE, and VSE, and GJR-GARCH (1,1) for Gold. It discussed volatility persistence and leverage effects during the Red Sea War, considering model sensitivities, geopolitical impacts, and research limitations. This research's practical and theoretical implications inform financial risk management, highlighting the importance of robust volatility modeling. The findings can enhance portfolio optimization and investment strategies during geopolitical instability. These insights underscore the need for sophisticated approaches to manage financial risks effectively. This research uses advanced GARCH models to analyze volatility during the Red Sea Crisis, revealing asset-specific behaviors under geopolitical stress. The findings improve financial risk management and investment strategies in unstable conditions, enhancing volatility predictions and professional practices.

IMPACT OF THE RUSSIA-UKRAINE CONFLICT ON INDONESIAN COMMODITY STOCKS: A SYSTEMATIC LITERATURE REVIEW

The Russia-Ukraine conflict has significantly impacted Indonesian commodity stocks, as revealed by a comprehensive literature review. The conflict has introduced volatility in global markets, directly affecting Indonesian stock commodity-based prices, exchange rates, and investor sentiment. While initial commodity price rises benefited Indonesian stocks, persistent volatility and market interconnectedness pose challenges. This paper employs a systematic literature review methodology, collecting, synthesizing, and analyzing numerous papers published in the last decade on a variety of topics closely related to volatility transmission issues. Based on our review of twenty-seven articles, the result of this paper's analysis underscores the importance of diversifying investments and enhancing resilience against global disruptions. The GARCH models and the Geopolitical Risk Index further emphasize Indonesian commodity stocks' susceptibility to global price fluctuations and geopolitical risks. The research concludes that the Russia-Ukraine conflict has far-reaching consequences for Indonesian stock commodity-based markets, underscoring the need for risk mitigation strategies and enhanced resilience against future geopolitical shocks.

The Financial Feasibility of Investing in a Sustainability Index in India

ESG is at the center of dialogue for investments, corporate governance and management. It used as an acronym for ‘Environmental, Social and Governance’ factors. The rapid rise of ESG has led to the prominence of socially responsible investing and has created a dual objective for investor’s capital. Researchers have previously studied the volatility of sustainable investments and whether SRI affects portfolio performance. Analyzing the financial feasibility of investing in a sustainability index was done by utilizing the historical data of returns on BSE ESG Index, NIFTY 500 Index and the BSE ESG 100 Index from October 2017 to April 2024. S&P BSE 100 ESG Index was used as a proxy for a sustainability index and the S&P BSE 100 Index was the index used to proxy traditional investments. This study has utilized financial ratios along with EGARCH and Granger causality test to understand the risk-reward characteristics of sustainable investments and compare them with traditional investments to understand whether it is financially viable to be a sustainably responsible investor. T test was conducted to identify whether is a significant difference between the returns of the sustainability index and its parent index. Results suggest that the sustainability index has a lower risk-adjusted return than traditional investments however, there is not a significant difference between the returns of the sustainability index and its parent index. There is a presence of strong persistent volatility in the sustainability index and negative shocks have a greater impact on the volatility of the index. Data also suggests a bi-directional relationship between the S&P 500 ESG Index and India’s sustainability index. The study recommends investors to consider the cost associated with investing in sustainability themed instruments while also taking into considerations the factors which are responsible for volatility of sustainable investments as well as the asymmetrical impact of negative shocks on returns.

Research on macroeconomic indicators and stock market correlation analysis based on machine learning

The stock market is known as the barometer of the national economy, and macroeconomic factors have an important impact on the volatility of the stock market. Therefore, considering the impact of macroeconomic factors on the sustainability of stock market volatility will help to capture the time-varying characteristics of volatility persistence, so as to significantly improve the estimation and forecasting effect of volatility. In this work, the generalized autoregressive conditional heteroskedasticity-mixing data sampling (GARCH-MIDAS) model is adopted, which combines the advantages of the GARCH model in short-term volatility modeling and the advantages of MIDAS regression in integrating macroeconomic variables of different frequencies. The GARCH model provides an accurate depiction of intraday volatility in financial markets by capturing the dynamic nature of short-term volatility. MIDAS regression introduces long-term macroeconomic factors into volatility modeling by integrating macroeconomic data with different sampling frequencies, thus making up for the shortcomings of traditional measurement methods in frequency matching. By combining these two methods, the GARCH-MIDAS model can more comprehensively reflect the dynamic changes of stock market volatility, considering both the impact of short-term market volatility and the role of long-term macroeconomic factors, thus providing a more accurate and in-depth analytical tool for volatility prediction and risk management. The results show that the GARCH-MIDAS model can significantly improve the accuracy of volatility forecasting, and provide more reliable decision support for investors, policymakers and economists.

Between Exchange Rate Returns and Crude Oil Returns in Oil-Importing Countries

Fluctuations in the financial markets stem from the reactions of investors to both market activities and more wide-ranging macroeconomic indices. This research examines the interconnectivity between returns on exchange rates and crude oil prices for ten oil-importing countries. Quarterly data spanning the period from 2000Q1 to 2022Q4 was used in the estimation. Returns had to be calculated from raw data, which were exchange rates and crude oil prices. The research methodologies include quantile regression and VAR-GARCH estimations. The study revealed a long-term association between crude oil market returns and foreign exchange markets, with currency fluctuations negatively impacting crude oil returns. The spill-over effect from the currency market to the oil market is weaker than the transmission effect from the oil market to the currency market of oil-importing countries. In particular, it was empirically established that shocks from past volatility in the currency markets of countries that import oil had commensurately lower volatility in the oil market by 6 percent. Whereas, the spillover from the crude oil market to the currency market depicts that increased turbulence on the crude oil market in the current period stimulated 55.66 percent amplified volatility in the currency market for oil-importing nations. Oil returns had a volatility persistence value of 0.1255, ratifying the weak volatility persistence of oil returns. The size of the volatility persistence of exchange rate returns is 0.997, an indication of high volatility persistence for currency returns. The study found the absence of leverage effects for currency values given a positive coefficient with a magnitude of 0.8529. In other words, bad news does not cause higher turbulence in exchange rate returns than good news will. For crude oil returns, the size of the leverage effects term is -0.0529, which is negative and significant (p<.05), implying that bad news in oil-importing economies has asymmetric impacts on the volatility of returns on crude oil prices. In effect, the oil price returns react more strongly to bad news than these returns react to good news. The findings of the study underscore the intricate relationships between local currencies and energy prices within the context of a global financial market, highlighting the significance of understanding these dynamics for effective decision-making and risk management in an increasingly interconnected world. It was recommended, among others, that international collaboration amongst countries is crucial given the global nature of energy markets.

Beyond the glitter: An empirical assessment of the true risk and hedging role of precious metals

Precious metals are an essential component of asset class within the global financial market offering diverse benefits. Being significant store of value, they are immune to default risk, provide hedge against inflation and are deemed to be safe haven. This highlights the importance of precious metals in the broader context of financial and economic stability. This paper focuses on the S&P Precious Metal Index (PMI) to explore its resilience towards financial market risks and examines if it could offer effective hedge during crises. Using Markov Regime Switching model, we find significant volatility persistence in PMI returns undermining its suitability for dynamic hedging. However, absence of speculative trends makes the PMI a safe asset. Further, financial market stress, as revealed by estimated Component GARCH and Discrete Threshold Models, does not affect the unique and market risk of investing in PMI. Although we could not establish PMI as a strong safe haven or a zero-beta asset, its potential to be included in a risk-avert investor's portfolio cannot be negated. Its consistently low and stress-invariant time-varying beta safeguards wealth against losses. The role of PMI becomes particularly salient while considering portfolio optimization during crises. In recent years, particularly during and after the pandemic the hedging effectiveness of PMI has increased. There is, however, a point of concern. The market risk of PMI is explained mostly by its lagged values rather than exogenous factors like market stress. Given the endogenous nature of market risks, chaotic dynamics might be in operation making forecasting and regulating PMI prices difficult.

Understanding climate policy uncertainty: Evidence from temporal and spatial domains

This study examines the dynamic propagation process of Climate Policy Uncertainty (CPU) on commodity markets' positive and negative volatility, and compares the role of different climate policy uncertainty. The climate policy uncertainty considered here includes the American Climate Policy Uncertainty (ACPU), Chinese Climate Policy Uncertainty (CCPU), and Global Climate Policy Uncertainty (GCPU). We find that CPU plays an informative role in commodities markets, both for the commodity markets' positive and negative volatility. Notably, we observe a more pronounced spillover effect of CPUs on negative volatility compared to positive volatility, indicating heightened market sensitivity to adverse policy uncertainty shocks. As for the spillover speed, the transmission of CPUs to positive volatility initially outpaces that to negative volatility, but this trend reverses over time as the persistence of negative volatility becomes more dominant. Moreover, a comparative analysis across different CPUs underscores the preeminent role of CCPU, which exhibits the broadest spillover scale and the most rapid transmission velocity relative to its American and global counterparts.

Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A Pre- and Post-Pandemic Analysis Using VAR-BEKK-GARCH Model

This study examines how the COVID-19 pandemic impacted stock market volatility and interconnectedness between India and other selected global economies. The analysis, using data from 2016 to 2024, reveals a substantial rise in volatility within both the Indian market and those of several other countries after the pandemic. Interestingly, the volatility transmission patterns also changed. While the Indian market’s volatility significantly influenced Brazil, China, and Mexico throughout the entire period, the influence of the US market became negligible post-pandemic. In contrast, Russia exhibited a weak but statistically significant impact on India’s volatility only after the pandemic. These findings highlight the lasting impact of the pandemic on global financial markets and emphasize the need for investors and policymakers to adapt. By understanding these new dynamics, investors can make more informed decisions, and policymakers can develop stronger risk management strategies and international coordination during periods of increased volatility. This study offers valuable insights for navigating the current financial landscape and the interconnectedness of emerging economies.

An empirical analysis of the volume-volatility nexus in crude oil markets under structural breaks: Implications for forecasting

The present study investigates how volume impacts volatility in crude oil markets, focusing on the Mixture of Distribution Hypothesis (MDH). Our results reveal a positive and significant volatility-volume association in the case of WTI spot markets. However, we cannot draw similar conclusions for Brent crude oil. Furthermore, we find that the presence of volume leads to only a negligible drop in the volatility persistence, thereby rejecting the claims of the MDH. By incorporating volume as a predictor for volatility and modelling the innovations using heavy-tailed distributions, our estimates of volatility for both Brent and WTI crude oil improve significantly. Our findings reveal that volume contains important information that can impact volatility and prices for crude oil. This has significant implications for market participants, institutional investors, as well as regulators as they can design better strategies and policies based on the volume of trade. Also, from the perspective of risk management and hedging, the results provide interesting insights. Our findings are consistent across different time periods, forecasting horizons and robust to alternative methodologies.

Impact of COVID-19 Crisis on Volatility Spillovers across Global Financial Markets: Evidence from Asymmetric GARCH Models

This study investigates the market volatility and asymmetric behavior in the commodity market, foreign exchange market, cryptocurrency, and stock markets by employing asymmetric GARCH models on the daily time series returns. The data covers the period from March 8, 2017, to March 17, 2023, and is divided in to three sub-periods: the entire sample period (March 8, 2017, to March 17, 2023), the pre-COVID-19 period (March 8, 2017, to March 10, 2020), and the during the COVID-19 period (March 11, 2020, to March 17, 2023). The empirical results show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Additionally, the results indicate significant positive asymmetric behavior in the crude oil and stock markets during the pandemic. The findings further document that gold exhibits a strong resilience during the pandemic period, indicating its hedging ability during crisis periods. Moreover, the results suggest that the EGARCH model is the most appropriate model to capture the volatilities of the financial markets both before and during the pandemic. The findings of this study provide useful insights for investors and policymakers, enabling them to adopt effective strategies for investing in portfolios during crisis periods in the future.

An empirical investigation of return spillover and volatility dynamics of Indian sectoral indices

ABSTRACT This research delves into the interconnectedness and volatility dynamics within sectoral indices amid the COVID-19 pandemic, to determine the most appropriate model for elucidating these dynamics. It addresses the burgeoning interest among investors in comprehending sectoral dynamics within India’s stock markets. Leveraging five years of daily closing price data (April 2017 to December 2021) sourced from the National Stock Exchange’s official website for 16 sectoral indices and the Nifty50 index, the study employs the Diebold-Yilmaz Spillover 2012 framework alongside four GARCH models (E-GARCH, T-GARCH, FIE-GARCH, and FE-GARCH). Empirical analysis underscores that the mid-small financial sector is the riskiest sector among others, advocating for the inclusion of the pharmaceutical sector to mitigate portfolio risks. Furthermore, the research underscores the persistence of volatility across sectors and identifies the TGARCH model as the appropriate model to capture the volatility for most sectoral indices.

Volatility Spillover from the Chinese Stock Market to the G20 Stock Markets in the Wake of the Pandemic COVID-19

We analyze the volatility spillover effect from the Chinese stock market to different stock markets in the G20 countries. We employ dynamic conditional correlation and vector autoregression (VAR) to analyze adjusted daily closing stock indices extending from 1st October 2019 to 30th June 2020. The result reveals that there is short-run volatility in sample stock return except Australia and South Korea. Similarly, there is long-term volatility persistence in sample countries’ stock exchange except Australia, Saudi Arabia, Russia, and France. However, Australia is only the country where there is no short- and long-run information transmission derived from China. Therefore, there is a portfolio diversification opportunity in this country during COVID-19. Overall, this paper shows significant interdependencies between the Chinese and the G20 markets which furnish momentous implications to the stakeholders of markets.

Impact of Derivative Trading on Stock Market Volatility: Empirical Evidence from Emerging Markets

This study investigated the impact of derivative trading on stock market volatility in emerging markets. We analyzed data from 10 emerging markets over 15 years (2005-2020) using a panel data approach. The research employed GARCH(1,1) models to estimate volatility and conducted difference-in-differences (DiD) analysis to assess the effect of derivative introduction. Additionally, we used Granger causality tests to examine the directional relationship between derivative trading and volatility. Results indicated that introducing derivatives significantly reduced stock market volatility (β = -0.0023, p < 0.01). This effect was more pronounced in markets with higher liquidity (interaction β = -0.0012, p < 0.05) and better regulatory frameworks (interaction β = -0.0015, p < 0.05). Granger causality tests suggested a unidirectional relationship, with derivative trading Granger-causing reduced volatility (F = 7.234, p = 0.002). Furthermore, GARCH persistence analysis revealed decreased volatility persistence post-derivative introduction across all sample markets. These findings contribute to the ongoing debate on the role of derivatives in financial markets and have important implications for policymakers and market regulators in emerging economies.

Analyzing fossil fuel commodities' return spillovers during the Russia and Ukraine crisis in the energy market

During the Russia and Ukraine crises, the energy market, including fossil fuel commodities, experienced significant global shocks. This research paper investigates the phenomenon of return spillover across the energy market amid the Russia and Ukraine conflict, utilizing the Diebold and Yilmaz Spillover Framework and EGRACH Model. The study examines daily frequency data from 24 February 2022 to 30 June 2023, focusing on the interconnectedness between various energy commodities: Brent Oil, Carbon Emissions, Crude Oil WTI, Gasoline, Heating Oil, and Natural Gas. The result of the study reports a total spillover index of 36%, indicating a significant spillover during the Russia and Ukraine conflict. Furthermore, the highest dynamic gross directional return spillovers have been reported in Brent Oil compared to the others. However, carbon emissions and natural gas have the lowest spillovers. The study significantly contributes to a deeper understanding of energy commodities by confirming the volatility persistence across the energy market based on the E-GARCH model.

The impact of the COVID 19 pandemic on stock market volatility: evidence from a selection of developed and emerging stock markets

This study examines the impact of the COVID 19 pandemic on the stock markets of China, India, Pakistan, the UK and the US using Generalised Autoregressive Conditional Heteroscedasticity (GARCH) and Threshold GARCH models with COVID 19 as an exogenous dummy variable in the variance equation. The sample period of 2016–2021 is divided into two sub-periods: the pre-COVID 19 period and the COVID 19 period. The results of the study indicate that there was persistent volatility in these markets and that this volatility increased as a result of the pandemic. In addition, the Threshold GARCH results indicate that the asymmetric term was significant in all markets indicating that bad news, such as the pandemic, had a stronger impact on the conditional variance of the returns as compared to good news. In addition, the results further confirm that the US market had no significant impact on the volatility of the Chinese market during the pandemic. The results have important implications for (1) international investors regarding portfolio management and investment risk minimisation in situations like the COVID 19 pandemic; and (2) policy-makers in terms of how they respond to any future pandemic.

Volatility Forecasting and Asymmetry Effect: Evidence from Dow Jones Sustainability Indices

The present study seeks to examine the varied asymmetric effects of Dow Jones Sustainability indices (DJSI) over a period from 1998 to 2020. Here, the study period is divided into three sub-periods (pre-recession, recession, and post-recession). The study applies ARCH and GARCH approaches to observe the nature of volatilities and their effect on their daily return. The study reports presence of significant asymmetric shocks and persistence of conditional volatilities in the daily returns during all the sub-periods. Moreover, leverage effects exist in the returns during the sub-periods except DJSI US. It is also observed that EGARCH and TARCH measures are appropriate in pre-recession and recession periods, but GARCH is gainful in post-recession.

Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100

The present study aimed to investigate the presence of asymmetric stochastic volatility and leverage effects within the Nasdaq-100 index. This index is widely regarded as an important indicator for investors. We focused on the nine leading stocks within the index, which are highly popular and hold significant weight in the investment world. These stocks are Netflix, PayPal, Google, Intel, Microsoft, Amazon, Tesla, Apple, and Meta. The study covered the period between 3 January 2017 and 30 January 2023, and we employed the EViews and WinBUGS applications to conduct the analysis. We began by calculating the logarithmic difference to obtain the return series. We then performed a sample test with 100,000 iterations, excluding the first 10,000 samples to eliminate the initial bias of the coefficients. This left us with 90,000 samples for analysis. Using the results of the asymmetric stochastic volatility model, we evaluated both the Nasdaq-100 index as a whole and the volatility persistence, predictability, and correlation levels of individual stocks. This allowed us to evaluate the ability of individual stocks to represent the characteristics of the Nasdaq-100 index. Our findings revealed a dense clustering of volatility, both for the Nasdaq-100 index and the nine individual stocks. We observed that this volatility is continuous but has a predictable impact on variability. Moreover, apart from Intel, all the stocks in the model exhibited both leverage effects and the presence of asymmetric relationships, as did the Nasdaq-100 index. Overall, our results show that the characteristics of stocks in the model are like the volatility characteristic of the Nasdaq-100 index and can represent it.