GARCH Research Articles

A study on the co-movement and influencing factors of stock markets between India and the other Asia–Pacific countries

PurposeThe aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.Design/methodology/approachUnit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.FindingsThe empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.Practical implicationsThe findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.Originality/valueThis study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.

Assessing the Forecasting Performance of GARCH Models in the Presence of Instabilities

Assessing the Forecasting Performance of GARCH Models in the Presence of Instabilities

Monetary momentum and risk management in stock market

Purpose- This study aims to investigate the relationship between monetary interest rate decisions, liquidity mechanisms and risk management issues. As a core interest the significance of a structural change in the data around the FOMC meetings is analyzed. By the help of continuous time models we analyze the kind of dynamics, which can be observed in the stock returns, i.e. conditional volatilities and jumps. A further central interest is given to investment decisions and risk management issues. This encompasses the elaboration of the hedging strategies to achieve higher performance. Methodology- The study employs GARCH-Ito and GARCH-Ito-Jump models to analyze the stock market returns and their related volatilities on the day of a FED interest decision announcement. The continuous time GARCH model setting allows to model stock market returns with a high flexibility, therefore these models are abled to capture jump dynamics in the stock returns. Findings- The analysis reveals that persistence in conditional volatilities change according to alternative stocks. These stocks can be classified according to alternative market capitalization sizes. Mega market capitalization stocks are better governed by no jump GARCH-Ito models regardless the monetary policy changes, that is, changes in interest rates or not. Conclusion- Based upon the analysis, it may be concluded that risk management applications effectively might perform under the consideration of stock types in terms of market sizes. The persistence in the conditional volatility massively decreases if a jump component is introduced into the model. Since most of the mega market cap stocks perform better without a jump part component, it might be conjectured that persistence in the conditional volatility for mega cap stocks play a more important role compared to large cap stocks. Regardless the case whether there is an interest rate change or not, the persistence in conditional volatility remains in mega cap stocks, and thus, these stocks are prone to the involvement of prices jumps. Keywords: Monetary policy, risk management, jump detection, investment decisions JEL Codes: C22, E49, G11

Volatility Spill-Over and Financial Contagion Effect of Conventional Stock on Islamic Equity in Nigeria: Evidence from Covid-19

This study empirically investigated volatility spill-over and financial contagion effect between conventional stocks and shariah compliant equities before and during covid-19 pandemic in Nigeria. Banking and insurance sectoral stocks indices were selected to represent conventional equities while Nigerian stock exchange lotus islamic index represent islamic stocks. Daily closing stock price data from 02-01-2018 to 26-02-2020 for pre-covid and 27-02-2020 to 31-12-2021 for pre-covid and during covid were considered. DCC-GARCH model (Dynamic conditional correlation- Generalized Autoregressive Conditional Heteroskedasticity Model) was deployed to examine the spill-over effect while t-test was applied to check the consistency of the DCC coefficient before and during the pandemic. The result of the multivariate GARCH show that both banking and insurance stocks have long run volatility spillover effect on lotus islamic index, the result also revealed no significant increase in conditional correlation during covid-19 pandemic which indicate absence of financial contagion from the conventional to islamic stocks. The results provide important information to researchers, investors, and policymakers

The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East Aftican Community

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH MIDAS model improved its forecasting ability, however, for Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCHMIDAS model.

PERAMALAN VOLATILITAS RETURN NILAI TUKAR RUPIAH TERHADAP US DOLLAR MENGGUNAKAN METODE EGARCH, TGARCH, DAN APARCH

Exchange rates play a crucial role among macroeconomic variables, exerting a significant influence on a country's economic landscape. Fluctuations in these rates can impact a nation's stability and economic activities. Consequently, it becomes essential to engage in forecasting endeavors, particularly in predicting the exchange rate of the rupiah against foreign currencies, with a focus on the US dollar. Certain instances in financial data reveal an asymmetric volatility response, often referred to as the leverage effect. To address this challenge, asymmetric GARCH models, including EGARCH, TGARCH, and APARCH, prove instrumental. This research endeavors to identify the most effective model among EGARCH, TGARCH, and APARCH using data pertaining to the rupiah's exchange rate against the US Dollar from March 2, 2020, to June 2, 2022. The findings indicate that the APARCH (1,2) model stands out as the optimal choice for predicting volatility, boasting the smallest AIC value in comparison to its counterparts. As per the research outcomes, volatility witnessed a decline from the initial day to the fourteenth day.

Numerical investigation of elastic-plastic buckling performance of circular arched cellular steel beam using nonlinear finite element analysis method

This study presents a numerical investigation of the in-plane elastic-plastic performance, post-buckling mode, and arched web-post shear resistance of a pinned end circular arched cellular steel beam using ABAQUS nonlinear finite element analysis package. The trustworthiness of the finite element analysis results was confirmed by comparing them to the existing experimental investigation results. The main study parameters, such as the effect of a rise-to-span ratio, the radius of curvature, the impact of opening, the types of loading on elastic-plastic performance, and the buckling mode of an arched cellular steel beam, were investigated. Furthermore, the arched web-post finite element model was proposed and the shear resistance of the arched web-post was investigated. Also, the appropriateness of the currently existing different web-post shear resistance analysis approaches was reviewed and evaluated in determining the shear resistance of arched web-posts. The results showed that the web post-structural stiffness of a circular arched cellular steel beam was improved as the rise-to-span ratio increased under the mid-span concentrated load regardless of the rise-to-span ratio. However, under uniformly distributed vertical load, increasing a rise-to-span ratio beyond 0.35 or 140° subtended angles reduces the stiffness of circular arched cellular steel beams. The web post shear resistance analyzing approaches proposed by Panedpojaman et al. and SCI P-100 overestimate and yield unsafe results in determining the web-post shear resistance of arched web post cellular steel of low rise-to-span ratio.

Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model

The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange's Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.

More different than alike: cross-sector volatility spillovers in Chinese stock sectors during COVID-19 pandemic

ABSTRACT The asymmetric spillovers and dynamic connectedness among sectors in Chinese stock market have not been fully studied, especially the impact of catastrophic events related to the COVID-19 pandemic on the linkages. In this paper, an asymmetric VAR-BEKK (DCC) – GARCH model is employed to measure the volatility spillovers and dynamic correlations among Chinese stock sectors before and during the COVID-19 pandemic. The empirical results indicate that volatility spillovers among sectors are significant, with evident differences before and during COVID-19. To be specific, the primary spillover transmitting sector has shifted from telecom business for the pre-pandemic period to the financial real estate sector after the outbreak. However, the role of the raw materials sector as the spillover receiver has not changed. Notably, the medical health sector forms closed-loop chains of transmission with the most other sectors during the COVID-19 period. In addition, asymmetric volatility spillovers and dynamic correlations among stock sectors are also significantly examined. The findings contribute to investors and decision makers in adjusting trading strategies and monitoring market risks.

Spillover Analysis of Carbon Market, Crude Oil Market, and Coal Market based on BEKK-GARCH

Global warming has become an urgent issue for all mankind, and climate change caused by carbon dioxide emissions is a growing concern. China, as a major global energy consumer, has set "carbon neutrality" as its vision for 2035. This study aims to explore the impact of carbon neutral policy on the volatility of the energy market, and through a comprehensive analysis of the historical data of the carbon market, crude oil market, and power coal market returns, we aim to reveal the volatility spillover effect among markets. In this study, we determine the distribution trend of yield receipts as t-distribution by performing statistical tests, ADF analysis on the yield historical data of the three markets. Under this premise, ARCH model, GARCH model and EGARCH model are further used to carry out the preliminary validation of the yield series of each market, and under the premise of ensuring the existence of volatility benefits in the data of each market, BEKK-GARCH model is used to carry out the analysis of multi-market spillover benefits, and the significance of the results is ensured by the WALD test. Finally, the results of the study are linked to actual policies and market operations to provide relevant recommendations for governments, investors, enterprises and carbon trading platforms. This study provides in-depth market perspectives and policy support for addressing the challenge of climate warming, and provides strong data support and decision-making reference for achieving the goal of carbon neutrality.

Disclosure-based regulation and municipal security trade prices

Purpose This paper aims to measure the trade price impact of a recent regulatory disclosure intervention in municipal securities secondary markets, which required broker-dealers to disclose securities trading information on a near-real-time and continuing basis. Design/methodology/approach The author analyzes trade price outcomes in the preintervention and postintervention regimes using a suite of time series estimations that give heteroskedasticity-robust standard errors (Prais–Winsten and Cochrain–Orcutt), accommodate higher-order lag structure in the error term (autoregressive integrated moving average) and account for volatility clustering in the time series (generalized autoregressive conditional heteroskedasticity). Findings Results show that regulatory disclosure intervention significantly improved trade price efficiency in municipal securities secondary markets as daily trade price differential and volatility both declined market-wide after the disclosure intervention. Research limitations/implications The sample consists of trades in State of California general obligation bonds; therefore, empirical findings may not be generalizable to other states, local governments and different types of bonds. Practical implications The findings highlight voluntary information disclosure as a practical and effective mechanism in disclosure regulation of municipal securities secondary markets. Originality/value Only a small body of work exists that examines information disclosure regulation in municipal securities secondary markets; therefore, this paper expands knowledge on the topic and should provide renewed impetus for regulatory efforts aimed at improving the efficiency of municipal capital markets.

Asymmetric beta-binomial GARCH models for time series with bounded support

In this paper, we introduce a new class of asymmetric beta-binomial generalized autoregressive conditional heteroscedastic (GARCH) models for bounded integer-valued time series, which can capture the asymmetric impact of positive and negative observations. We study the stationarity conditions of the process and derive the moment and covariance functions. Furthermore, we estimate the unknown parameters using the conditional maximum likelihood (CML) method. The asymptotic properties of the estimators are discussed, as well as their finite-sample performance. Finally, we illustrate the model to real time series data in the field of meteorology.

Comparison of Mechanical Behavior of Clear Aligner and Rapid Palatal Expander on Transverse Plane: An In Vitro Study.

(1) Background: This study aims to investigate, within a controlled laboratory environment, the magnitude of the transversal load and the force decay over time produced by clear aligners in comparison to a Rapid Palatal Expander (RPE). (2) Methods: Resin models of a dental maxillary arch, additively manufactured from an intraoral scan, were inserted in a testing machine with uniaxial load cells to measure the force trend over time expressed by RPE and clear aligners. The mechanical load was recorded during a certain timeframe for both appliances. (3) Results: The force expressed by the RPE ranged from 30 to 50 N for each activation, decreasing with a nonlinear pattern over time. The force expressed by the clear aligner ranged from 3 to 5 N, decreasing with a linear pattern over time. In contrast, the force generated by the clear aligner fell within the range of 3 to 5 N, showing a linear reduction in force magnitude over the observed period of time. (4) Conclusions: The RPE exerted a force magnitude approximately ten times greater than that generated by clear aligners. Nevertheless, it is essential to acknowledge that the oral environment can significantly influence these results. These limitations underscore the need for caution when applying these findings to clinical settings.

Unveiling hidden connections: Spillover among BRICS' cryptocurrency-implied exchange rate discounts and US financial markets

This study employs connectedness approach based on Time-Varying Parameter Vector Autoregression (TVP-VAR) and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) methods, to scrutinize the connectedness between the cryptocurrency implied exchange rate discount in BRICS countries (Brazil, Russia, India, China, and South Africa) and US financial markets. Empirical evidence suggests that Bitcoin market volatility substantially influences other markets. Moreover, given the intimate connections between BRICS countries and developed nations such as the US in today's globalized context, fluctuations in the Bitcoin market within BRICS countries are apt to impact US financial markets. Our empirical findings specifically indicate the following: (i) Among the total spillover effects of the cryptocurrency implied exchange rate discount in BRICS countries, Russia exhibits the most potent total spillover effect and contributes the most to market connectedness. (ii) The Brazil directional spillover index is elevated in both spillover relationship categories, and the spillover effect of the cryptocurrency implied exchange rate discount in Brazil on US financial variables is notably pronounced. (iii) Of the US financial variables, the US Financial Stress Index demonstrates the most robust connectedness with the cryptocurrency implied exchange rate discount in BRICS countries and exhibits heightened sensitivity to uncertainty.

Predictive Analysis Based on Prophet Model: Evidence from the Number of Epidemic Infections

This study makes a prediction of infection numbers in the future and investigates the correlation between the new cases and the stock price of companies in three different fields. In the first part of the prediction, we use three methods to make predictions of the future infected number, including the ETS, Auto-Regressive Moving Average (ARIMA), and Prophet model. In the next part, we use the GARCH model to discuss the correlation between infection numbers and stock prices. Considering the heterogeneity, we choose three different companies’ stocks to represent three industries respectively. They are Apple, Amazon and Pfizer. The result indicates that the stock price of Apple is negatively correlated to the infection number, the relationship between the infection number and the price of Amazon is inconspicuous, while the price of Pfizer is positively correlated with the development of the pandemic. Thus, we draw the conclusion that the impact of the epidemic is strong to manufacturing, is not obvious to the Internet industry, and boosts the development of the medical corporation.

How risky are cryptocurrencies?

ABSTRACT Volatility in the cryptocurrency market is an extremely important indicator for investors, as it allows them to manage their investment risk and define strategies that will result in profit maximization. Thus, this study focuses on determine which of the GARCH, EGARCH, and TGARCH models is the optimal model that best describes the daily returns’ volatility of MATIC, SOL, BTT, and VET – four cryptocurrencies with relatively limited presence in the market compared to Bitcoin. The optimal model is selected by using the AIC statistical quality criterion. For the chosen sample period, empirical evidence suggests that EGARCH(1,1) and GARCH(1,1) are the most suitable models for describing the returns’ volatility of MATIC and VET, respectively. In the cases of SOL and BTT, the lack of success in validating all the assumptions needed to apply GARCH models reveals that these models are not the most adequate for describing the cryptocurrencies under study.

VaR and ES forecasting via recurrent neural network-based stateful models

Due to the widespread and quickly escalating effects of large negative returns, as well as due to the increase in the importance of regulatory framework for financial institutions, the accurate measurement of financial risks has become a relevant question in the academia and industry. This paper proposes three novel models based on stateful Recurrent Neural Networks (RNN) and Feed-Forward Neural Networks (FNN) to build forecasts for Value-at-Risk (VaR) and Expected Shortfall (ES). We apply the models to six asset return time series spanning over more than 20 years. Our results reveal that the RNN-based stateful models generally outperform the non-stateful RNN models and econometric benchmark models including rolling window models, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH)-type models, and Generalized Autoregressive Score (GAS) models, in terms of VaR and ES forecasting.

Price volatility characteristics and policy implications of freshwater fish in China

The fisheries industry in China is experiencing rapid growth, but it is also encountering the challenge of frequent fluctuations in aquatic product prices. Freshwater fish aquaculture plays a crucial role in China’s fisheries industry. Analyzing the characteristics of price volatility in freshwater fish is crucial to ensure a stable supply of aquatic products and promote sustainable fisheries industry development. This study focuses on four freshwater fish species, namely crucian carp, grass carp, silver carp, and carp, to analyze the price volatility characteristics of freshwater fish in China from 2006 to 2022. The study uses the price decomposition method and the ARCH model. The results indicate that, from the perspective of dominant characteristics of price volatility, the prices of freshwater fish display periodic volatility with an overall upward trend, as well as significant seasonal and random variations. From the perspective of recessive price volatility characteristics, the price volatility of freshwater fish exhibits clustering and asymmetry. This indicates that the price volatility of freshwater fish is influenced by previous price volatility. Positive news has a greater impact on the price volatility of freshwater fish than negative news. Out of the four freshwater fish species, only the price of crucian carp is characterized by high-risk and high-return volatility.

High-Frequency Volatility Modeling of Chinese Agricultural Products Market Based on Garch-Type Models

The modeling of high-frequency volatility is of utmost importance in comprehending market dynamics and the characteristics of risk. The adoption of high-frequency volatility modeling in the agricultural sector has the potential to enhance risk management for food production enterprises through the utilization of hedging strategies to mitigate the impact of food price changes. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) class of models is well-suited for the analysis and prediction of volatility in financial time series. These models effectively capture the highest level of volatility observed in the time series data. Therefore, this research utilizes one-minute trade data of the Wind Agriculture Index (886045.WI) to examine the efficacy and predictability of several GARCH class volatility models. When it comes to fitting the data within the sample, the TGARCH model demonstrates superior performance compared to both the GARCH and EGARCH models. The fluctuations in the price changes of the Wind Agriculture Index exhibit characteristics of time variability and clustering, which can be attributed to the relatively low barriers for entry and exit in the agricultural planting sector. Simultaneously, within the agricultural market, an imbalance is observed whereby the influence of positive information on price volatility surpasses that of negative information.

Return comovement and price volatility: a study of the US dairy commodity futures markets

ABSTRACT US dairy markets have become increasingly volatile, which presents a challenge for dairy farmers and industry participants to manage risk, disturbing the stability of the industry. However, the study of dairy volatility is limited. This article investigates the return comovement and price volatility of four major US dairy commodities: butter, cheese, Class III milk, and dry whey. We also investigate the COVID-19 pandemic’s impact on dairy volatility. Our results show that dairy commodities returns and volatilities are positively correlated, but only cheese and Class III milk correlate strongly. The volatilities of butter, cheese, and Class III milk are impacted comparably by return shocks and past volatility. However, dry whey volatility is predominantly driven by past volatility. Using multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models, we demonstrate that return comovements are time-varying and volatilities are interdependent among dairy commodities. Volatility spillover effects are observed among dairy commodities, especially between dry whey and cheese. The COVID-19 pandemic amplifies dairy volatilities and spillover effects, with a more substantial impact on cheese and milk. Interestingly, dry whey experiences the least impact and performs better during the pandemic. Our results aid market participants in risk management and inform policymakers’ decision-making processes.