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The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence.

This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (

The main aim of this research study is to examine the behavior of the European developed stock market from France during the period that covers the COVID-19 pandemic and the war between Russia and Ukraine. The empirical analysis is based on selected CAC 40 stock index from the developed stock market in France during the sample period from January 2018 to December 2023. The empirical results provide an overview of the behavior of the French stock market in the selected period. [ABSTRACT FROM AUTHOR]

The main aim of this research study is to conduct an empirical analysis regarding the behavior of the Asian stock markets in under the impact of turbulent events based on a case study for Japan. The analyzed period selected for this study covers the sample period from September 2006 until September 2023, including daily observations. Moreover, the sample period also covers the turbulent period of certain extreme events such as the global financial crisis of 2007-2008 and the COVID-19 pandemic. This empirical research study makes an obvious contribution to the existing literature on the long-term behavior of Asian stock markets. [ABSTRACT FROM AUTHOR]

Stocks play a crucial role in the stock market, which is at the very core of every nation's economic growth. Investors, analysts, and others in related fields have turned their attention to stock price analysis. This study empirically investigates the conditional variance (volatility) or empirically estimates the price volatility spillover transmission in the daily returns of IPC Mexico index from Mexico stock market, for the long period January 1993 - July 2023 (which is more than 30 years daily data) using the GJR- GARCH model. There are 7661 daily observations included in the study. The recurrence of outcomes had leptokurtosis, were skewed to the left, and were not normally scattered; there was also confirmation of ARCH effects. The insights of the symmetric GARCH model showed confirmation of volatility clustering and endurance; the results of the GARCH-GJR model demonstrated the leverage impact and volatility clustering in the sample index. Favorable events alter the conditional variance (volatility), resulting in significant asymmetric GJR-GARCH values. This leads to the conclusion that favorable information has greater effects on index return volatility than adverse information. The results of this research constitute vital information for financiers, risk analysts, and regulators. [ABSTRACT FROM AUTHOR]

GARCH (Generalize Autoregressive Conditional Heteroscedasticity) family models, notably the GARCH (1,1), GJR-GARCH,EGARCH, M GARCH, and TGARCH models,(Chang, McAleer, & Tansuchat, 2012) are used in this study to empirically test volatility clusters of the IBOVESPA index using data from 20 May 1993 to 17 March 2023. A further perspective on asymmetric volatility clustering and leptokurtosis patterns is offered by the empirical analysis, which is based on daily observations from the Brazil stock market (7380). The study examines the existence of asymmetries in the patterns that transmit volatility, the movement of shocks with higher positive and negative magnitudes, and the suitability of the model. In addition, the purpose of this study is to evaluate the accuracy of volatility forecasts using both univariate and multivariate models that of Normal, Student-t, and Generalized Error Distribution constructs in predicting stock volatility over a period of more than 30 years, from May 1993 to March 2023 in the Brazilian stock markets. The results supported the existence of the leverage effect during the sampling period. Furthermore, the empirical results revealed that the sample returns of the chosen stock market had a significant level of volatility. By adding new factual data on the long-term performance of the Brazilian stock market, particularly in light of exaggerated events, this study adds to the body of literature already in existence. By adding new empirical data on the long-term behavior of the Brazilian stock market, particularly in light of extraordinary occurrences, this study adds to the body of literature already in existence. [ABSTRACT FROM AUTHOR]

The purpose of the empirical research study is to analyze the volatility of OMX Tallinn Index in Estonia from 2002 to 2022 in three major period phases i.e. 2002-2008, 2009-2015 and 2016 to 2022. Moreover, it attempted to formulate PARCH Model for each phases of OMX Tallinn Index in Estonia from 2002 to 2022 that could grasp not only the volatility but also asymmetric volatility caused by various important events for each particular period. The total sample size is 6,032 i.e. 3 phases of 1826 observations each. The selected period covers a series of extreme events such as the global financial crisis, the COVID-19 pandemic, the war between Russia and Ukraine that began in 2021 and others. The empirical results are relevant and contribute to the existing literature. [ABSTRACT FROM AUTHOR]

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has emerged as one of the most popular themes in financial econometrics and has been mainly and constantly used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives, are all part of the process. The purpose of this paper is to contrast the GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) family models -- GARCH, PGARCH, EGARCH, GJR-GARCH, MGARCH, and IGARCH -- with the EWMA (Exponentially Weighted Moving Average) models in the prospect of establishing the best algorithm to forecast TSEC volatility weighted index from Taiwan stock market. We employ daily returns for the range between July 1997 and July 2023. The sample data spans a long time period i.e. 26 years of data with 6375 observations, which includes dramatic events such as the financial crisis of 2008, the COVID-19 epidemic, and the war between Russia and Ukraine. We observe that the asymmetric model PGARCH with a Student's t distribution develops the best model for assessing the volatility of the chosen index, but PGARCH with a normal error distribution produces the best estimating performance outcomes and hence outperforms the EWMA model. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Taiwan, together with a deeper exploration of the TSEC weighted index from Taiwan stock market volatility dynamics, given the scarcity of prior such studies. [ABSTRACT FROM AUTHOR]