Search

The objective of this article is to examine the volatility spillover effect between the foreign exchange market and the stock market of Brazil, Russia, India, China and South Africa (BRICS) countries along with Japan as the developed country in the region, affecting the BRICS countries. Generalized Autoregressive Conditionally Heteroscedastic (GARCH) (1,1) method is used to study the volatility between the stock market and the foreign exchange market in selected countries, and asymmetric model, that is, Exponential Generalized Autoregressive Conditional Heteroscedasticity—EGARCH (1,1) is also used to investigate the presence of leverage effects in both stock market and foreign exchange market in selected countries. GARCH findings suggest a two-way volatility spillover between the stock market and foreign exchange markets for India, China and South Africa. In BRICS countries, volatility spillover from the currency market to the stock market is seen as more evident and robust as compared to spillover from the stock market to the currency market. A positive asymmetry in spillover is also observed from the foreign exchange market to the stock market. The findings of the study may provide valuable information to investors for decision-making in international portfolio investment and also for economic policymakers for their financial stability perspective. [ABSTRACT FROM AUTHOR]

This paper investigates five different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student's t, GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student's t-distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087. [ABSTRACT FROM AUTHOR]

Abstract Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Finance is the property of Academic Finance Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund "Golden Lev Index 30" stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the "EF Rapid" investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds. [ABSTRACT FROM AUTHOR]

AbstractThis study tests for calendar anomalies in returns for petroleum and petroleum products via the futures market, specifically, the day-of-the-week (DOW) effect. The energy future contracts in this study are the WTI (West Texas Intermediate), Brent, RBOB (Reformulated Blendstock for Oxygenate Blending) Gasoline, Heating Oil, and Natural Gas. Futures provide a more liquid insight into price movements relative to spot prices, where financial market participants can engage. We ensure the most appropriate price is used by focusing on the most liquid contracts by combining the front two months of the studied commodities nearing expiration. Our research shows that the DOW effect varies across the respective energy commodities; however, for investors engaged in trading these futures, our results may help time their trade decisions.

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cameroon Academy of Sciences Journal is the property of Cameroon Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund “Golden Lev Index 30” stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the “EF Rapid” investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds.

In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic indicators including inflation, and GDP per capita. And then we modeled the effect of widespread Russian economic sanctions on the value of the Russian ruble during the turbulent days of February 9 to April 9. Our research results show that there is not much difference between market value and fair value in the long run. Also, by observing the behavior of the ruble during the turbulent days of February 25, 2022, to April 26, 2022, and by entering the conditional risk factor and weighted average of the ruble, the USD to ruble equality between 76.23 and 91.6 was evaluated

Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future. [ABSTRACT FROM AUTHOR]

This study tests for calendar anomalies in returns for petroleum and petroleum products via the futures market, specifically, the day-of-the-week (DOW) effect. The energy future contracts in this study are the WTI (West Texas Intermediate), Brent, RBOB (Reformulated Blendstock for Oxygenate Blending) Gasoline, Heating Oil, and Natural Gas. Futures provide a more liquid insight into price movements relative to spot prices, where financial market participants can engage. We ensure the most appropriate price is used by focusing on the most liquid contracts by combining the front two months of the studied commodities nearing expiration. Our research shows that the DOW effect varies across the respective energy commodities; however, for investors engaged in trading these futures, our results may help time their trade decisions. [ABSTRACT FROM AUTHOR]

The study is an attempt to investigate the impact of crude prices on energy crops; Soyabean, Wheat and Sugarcane for India for the period Jan 2016-Dec 2020. The study mainly covered two dimensions; the spillover of volatility from crude to the energy crops and the asymmetric angle. The econometric tests employed under the study were GARCH(1,1), TGARCH(1) and AR(2)-EGARCH(1,1) Models. The results revealed that return on crude was impacting two crops; sugarcane and wheat while it had no impact on soyabean. The ‘T-GARCH’ results revealing asymmetry were statistically significant for all the three crops, however since soyabean did not have significant slope coefficient in Mean Equation, it was held that asymmetry held only for two crops viz. wheat and sugarcane and not for soyabean. Further coefficient of asymmetry for two crops was positive indicating that positive shock (fall in crude prices) impacted the crop volatility more than an equivalent negative shock (rise in crude prices). The results for spillover of volatility from crude to energy crops using AR(2)-EGARCH(1,1) Model showed that spillover from crude to both; sugarcane and wheat was statistically significant but coefficient in both crops had negative sign. Degree of volatility persistence was statistically significant for these two crops but its value was low implying that a shock to crude had a slow decaying impact on both these crops.

This study proposes an estimation technique for developing the best fit ARIMA-GARCH model to predict the closing values of Nifty 50. The study put forward di¤erent methods to resolve the issue of non-stationarity in mean as well as variance of the series before starting the estimation process. This study has applied autoregressive integrated moving- average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), ex- ponential GARCH (EGARCH) and threshold GARCH (TGARCH) model along with other estimation procedures on the daily closing prices of Nifty 50 from Jan 1, 2009 to Dec 30, 2019. Finally, the study identifies ARIMA(2,1,2)-EGARCH(1,1,1) as best model to predict the closing prices of Nifty 50. The findings indicate that the static forecast provides better results as compared to the dynamic forecast. These research findings will add to the tool kit of domestic as well as international portfolio managers and investors to frame suitable NIFTY trade strategies with least possible risks. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the RWH, return characteristics and various asymmetric effects of the daily returns of the DJSI (SRI) indices during pre-recession, recession, postrecession and the whole periods (December 1998 to March 2015). To achieve these objectives RWM, ARCH, GARCH, EGARCH and TARCH models are applied along with these various tests are done. ARCH measure confirms about the presence of volatility clustering. According to GARCH measure significant asymmetric shocks and persistence of conditional volatilities present in the daily returns of the SRI indices during the entire sub periods as well as the whole period. According to the EGARCH measure shows that the returns of the SRI indices are free from leverage effects except for DJSI Korea index where leverage effect exists during the recession period. For volatility forecasting not a single measure is appropriate based on various criterions (RMSE, MAE & MAPE). Only GARCH measure is appropriate during the post-recession period. It is also found that the standardised residuals are i.i.d. Finally, the returns of the SRI indices follow RWH that means the indices are informationally efficient in their weak forms and no one can predict the SRI stock price movements and earn abnormal profits by technical analysis.

For economists and investors, it is necessary to understand the random and nonlinear pattern of the stock market volatility. High volatility directly affects the financial market that leads to unpredictability. China–Pakistan Economic Corridor attracts economists and investors worldwide. Therefore, predicting the volatility of the stock markets related to CPEC is important. In this study we consider the most important stock markets lying on the route of CPEC, namely KSE 100 (Pakistan), SSE 100 (China), TADAWUL (Kingdom of Saudi Arabia), KASE (Kazakhstan), KLSE (Malaysia), BIST (Turkey), MOEX (Russia), FTSE (United Kingdom) and CAC40 (France). The daily returns of stock market indices consist of 1706 observations from December 2014 to July 2021. After the confirmation from the ARCH effect test, family GARCH models are employed, among them, based on AIC and BIC criteria, GARCH (1,1), EGARCH (1,1), and GARCH-M (1,1) are found suitable to forecast the volatility. The empirical study also suggests that the out-of-sample forecast GARCH-M (1,1) model is more appropriate as it has a minimum value of MAE, MSE, RMSE, MAPE, TheilU1, and Theil U2 among all the studied GARCH models. Furthermore, it is also found that the KSE-100 and SSE-100 have moderate and slow market average returns even though both stock markets are found to be the least risk-returns markets. [ABSTRACT FROM AUTHOR]

Copyright of Ekonomika is the property of Society of Economists 'Ekonomika' and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The January effect, and its impact in the financial world, is one of the most researched calendar irregularities. The purpose of this study is to examine if there is a January influence on the South African stock market. Aggregate and sectorial indices of Johannesburg Stock Exchange (JSE) were examined. We analyse the January effect by Generalized Auto Regressive Conditional Heteroskedasticity Model (GARCH), exponential GARCH (EGARCH) and threshold GARCH (TGARCH) models. Findings from the mean equation showed a positive January effect for the Top 40 and Basic materials, whilst for the variance equation, a negative January anomaly was found in the Top 40, Health care, Telecommunications, and Technology indices. The January seasonal investment style is recommended for improving returns in Basic materials sector. Investing in the Telecommunications sector in January will assist an investor in portfolio diversification and reduce risk. The findings show that the January effect has shifted to other months. Given the limits of previous research, this study adds to the body of knowledge on calendar anomalies by bringing a South African viewpoint to the debate of the January anomaly and extending the analytic period to 1995-2018. [ABSTRACT FROM AUTHOR]

In this study, we utilize various GARCH-type models to estimate and forecast volatility on S&P 500 returns and compare the results between the two financial crises, the GFC of 2008 (Global Financial Crisis of 2008) and the COVID-19 financial crisis. These two financial crises are different from the forming reasons by whether mainly caused by the financial factors. This study also makes the evaluations on the performance of these GARCH-type models in estimating and forecasting volatility, which may provide the efficient models for reference for the research of the volatility of the future potential financial crisis. We find that as for the AIC/BIC assessments on the estimation of volatility, the GJR-GARCH model performs better during the GFC of 2008, while the EGARCH model has the better performance during the COVID-19 financial crisis. With respect to the QLIKE loss function evaluation on the forecasting ability of volatility, the GJR-GARCH model performs better during the GFC of 2008, while symmetric GARCH model has better volatility forecasting ability during the COVID-19 financial crisis.

The holiday effect is one of the most noticeable seasonal anomalies in financial markets, and it appeals to technical traders who use past data from trading activities to devise profitable strategies. The purpose of this article is to assess the South African equity market's holiday anomaly. The financial data employed consists of market and sectorial indices of the Johannesburg Stock Exchange (JSE) spanning from 1995 to 2018. The holiday effect was modelled by Generalized Auto Regressive Conditional Heteroskedasticity Model (GARCH), exponential GARCH (EGARCH) and threshold GARCH (TGARCH) models. Negative pre-holiday effect was found in aggregate and sectorial indices for the variance equation. The mean and variance equations unveiled a post-holiday effect in market and sectorial indices. Basic materials should be avoided by investors since it increases their exposure. By investing in the Basic Materials sector, the pre-holiday trading strategy can help investors reduce their risk exposure. When it comes to post-holiday seasonal trading, investors should concentrate on the Telecommunications sector to generate higher profits. Investors can minimize losses by avoiding the Industrials sector, which increases risk exposure. In contrast to previous empirical research that focused on aggregate indices, our analysis delineates holiday anomaly for sectorial indices in South African equity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Social Research is the property of Journal of International Social Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The research aims to build a suitable model for the conditional mean and conditional variance for forecasting the rate of inflation in Pakistan by summarizing the properties of the series and characterizing its salient features. Design/Methodology/Approach: For this purpose, Pakistan’s Inflation Rate is based upon the Consumer Price Index (CPI), ranging from January 1962 to December 2019 has been analyzed. Augmented Dickey Fuller (ADF) test that was used for testing the stationarity of the series. The ARIMA modeling technique is a conditional mean and GARCH model for conditional variance. Models are selected on AIC and BIC model selection criteria. The estimating and forecasting ability of three ARIMA models with the GARCH (2,2) model has been compared to capture the possible nonlinearity present in the data. To depict the possible asymmetric effect in the conditional variance, two asymmetric GARCH models, EGARCH and TGARCH models have been applied. Findings: Based on statistical loss functions, GARCH (2,2) model is the best variance model for this series. The empirical results reveal that the performance of model-2 is best for all the three variance models. However, the GARCH model is the best as the variance model for this series. This shows that the asymmetric effect invariance is not so important for the rate of inflation in Pakistan. Implications/Originality/Value: The current study was based on the least considered variables and the pioneer in testing the complex relationship through the ARIMA model with GARCH innovation.

In this article, our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps. The first step includes modeling the data series by using EGARCH model alone, interspersed with steps of detecting the heteroscedasticity effect, estimating the model's parameters, and testing the adequacy of the model. Also, we predict the conditional variance and verify its convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in the modeling of time series data, predicting future values, and then assessing the prediction ability of the proposed model by using the prediction error criterion. [ABSTRACT FROM AUTHOR]

Using GARCH(p,q) models, in this study our aim is to examine and search the characteristics of volatility of Turkish sugar price. Due to the ARCH effects on price, ARCH(q), GARCH(p,q) and EGARCH(p,q) including these effects on mean and variance equations were estimated. Normal, t-Student, and generalized error distributions with Maximum Likelihood Estimation Method were estimated for these models. Determining the optimal parameters, Marquardt’s algorithm (1963) was used for maximizing the log-likelihood function. Mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute deviation (MAD) were used to determine the fit model for making predicting. In this study, we found the best model as a GARCH (1,1) model. [ABSTRACT FROM AUTHOR]

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news. [ABSTRACT FROM AUTHOR]

In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the differences between the systematic risk estimated by OLS and it simultaneous estimation with a GARCH errors. The systematic risk of an asset is estimated by the beta coefficient of the market line. According to the OLS hypothesis, the estimators are robust and residuals are white noise process. However various papers show the existence of statistical anomalies (stylized facts) in residuals (heteroskedasticity, autocorrelation and non‐normality) rejecting the BLUE properties of estimators. In order to considerate these anomalies to modelize the hazard in residuals regression, we use ARCH processes class that has proved it efficiency in finance. We estimate simultaneously the parameters of the market line and those of the GARCH process for the 30 perennial equities listed in CAC40 for the daily period 2005 to 2015 and we compare them each other. We select the E‐GARCH model providing the best residuals characteristics and we note significant differences with the OLS Betas particularly for equities with betas greater than 1. On this base, we estimate a linear relationship between the OLS Betas and the E‐GARCH Betas considering this break to modelize the differences between these two kinds of Betas. By this way, Investors can quickly adjust the Beta with this tool without completely reestimating them with GARCH. [ABSTRACT FROM AUTHOR]

In real life, the stock price is always easy to be mispriced after listing, too high or too low. There are numerous reasons behind mispricing. Many scholars believe that the information asymmetry is one of them. In an inefficient market, the information asymmetry is bound to exist and be very high. In order to investigate the impact of registration-based IPO system reforms in mispricing, this paper takes the GEM market as an example to analyze the impact of the GEM market on mispricing. And we proposed a method with fuzzy logic neural network for Growth Enterprise Market analysis for IPO system. After processing data, we use ARIMA and EGARCH model to find the results. The immaturity will result in the negative impact caused by reformation, which will deviate from the goal that makes Chinese stock market become better. [ABSTRACT FROM AUTHOR]

This study has the purpose to examine the effect of political events on the volatility of stocks traded on the Indonesia Stock Exchange (IDX). Furthermore, this study also sees whether such political events also influence the shares that have direct links with the participants in presidential elections. The political event being examined was the Indonesian Presidential Election held in 2014. The researchers used the daily data on the shares of all companies listed on the Indonesia Stock Exchange (IDX) in 2014. The hypothesis testing were done using the GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) estimation and its derivatives namely EGARCH (Exponential GARCH) and TARCH (Threshold GARCH). It was found that the 2014 Presidential Election asymmetrically affected stock return volatility on IDX and contrary to the leverage effect, which means that positive shocks (good news) have better influence than negative shocks (bad news). Out of all listed companies that have direct links with participants in the presidential election, 3 companies have their stock volatility affected by this Presidential Election; some with symmetric effect and some others with asymmetric effect.

January effect means that stock returns are higher in January than in any other month of the year. The aim of this study is to investigate whether there is a January effect on Borsa İstanbul (BIST 100) stock returns and volatility with GARCH, EGARCH and TGARCH models. For this purpose, the daily stock return closing values covering the period of 04.01.2010-18.02.2020 were used. According to the findings, while January effect was observed on stock returns, no effect was found on volatility. In addition, it was determined that negative shocks are more effective than positive shocks in EGARCH and TGARCH models created to determine leverage effect. [ABSTRACT FROM AUTHOR]

Copyright of Boğaziçi Journal: Review of Social, Economic & Administrative Studies is the property of Bogazici University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Semestre Económico is the property of Sello Editorial de la Universidad de Medellin and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The characteristics of understanding the asset's behavior is examined in terms of influence of its past information or shock on its return behavior on the different broad market indices from NSE (National Stock Exchange). The GARCH family models were used to find best model fit based on the Akaike Info Criteria and Schwarz Info Criteria. From the empirical results, it is found that all the characteristics in regard to the spillover, persistence of volatility and leverage effect are persuading the variance on the selected index spot return during the selected sample periods. [ABSTRACT FROM AUTHOR]

Research Background: The banking sector plays a crucial role in the world's economic development. This research paper evaluates the volatility spillover, symmetric, and asymmetric effects between the macroeconomic fundamentals, i.e., market risks, interest rates, exchange rates, and bank stock returns, for the listed banks of Pakistan. Purpose of the article: The main purpose of this study is to examine the volatility of Pakistani banking stock returns due to the influence of market risk, interest rates, and exchange rates. Pakistan is selected for the study because the volatility of its banking stock returns is strongly influential in achieving sustainable economic development. Methods: By applying the OLS with the Heteroskedasticity and Autocorrelation Consistent (HAC) covariance matrix, the GARCH (1, 2), and the EGARCH (1, 1), analysis is conducted for the period from January 1, 2009 to December 31, 2019 using samples of 13 listed banks. Findings & Value added: The ARCH parameter is significant in the OLS with the HAC covariance matrix estimation, which is a clear indication of the existence of heteroskedasticity in the squared residuals and the inaccuracy of the OLS with the HAC covariance matrix. The results of the OLS with the HAC covariance matrix suggest using the GARCH model family to accurately measure the volatility of bank stock prices. The results of the mean equation in the GARCH (1, 2) and EGARCH (1, 1) indicate the positive significance of market risk and the low significance of interest and exchange rates, confirming that market returns strongly affect the sensitivity of bank stock returns compared to interest and exchange rates. It should be noted that the ARCH (α) and GARCH (β) parameters of the variance equation fulfill the non-negative conditions of the GARCH model. Furthermore, the leverage parameter (λ) is found to be positively significant for all banks, and volatility is found to be influenced by positive shocks compared to negative shocks. Conclusively, it can be stated that market returns determine the dynamics of the conditional returns of bank stocks. Nevertheless, the interest and exchange rate volatilities determine the conditional bank stock returns' volatility. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the conditional volatility in the excess returns of the real estate investment trust index and Borsa Istanbul 100 index. Three models which are GARCH, EGARCH and GARCH-GJR to their daily excess return were applied and compared. Results showed that GARCH model fails to account for coefficient restrictions, asymmetry and leverage effect. EGARCH and GARCH-GJR succeed to encompass those limitations. Moreover, EGARCH is the most efficient model to estimate the conditional beta in this study. [ABSTRACT FROM AUTHOR]

This study aims to investigate the potential effects of structural breaks on volatility permanence and asymmetry in stock exchanges of fragile five countries. The study covers the stock market index of 12 countries that were evaluated as fragile fives by various investment institutions on different dates between January 2013 and November 2019. The GARCH and EGARCH models are used for volatility estimates, and ICSS iteration algorithm is used to detect structural breaks. Results of the study reveals that among the 12 stock exchanges Indonesia, India, Brazil, Russia and Turkey are the top five countries with persistent volatility in their stock market indices. According to the empirical results from the study, volatility models in which structural breaks were not included depicts that the permanence effect is high in Indonesia and India, however, it is relatively lower in Argentina and Egypt. In addition, asymmetric volatility and leverage effect was observed in all country indices except Argentina. While where the response to negative shocks are strongest the indices of Pakistan, Qatar, South Africa and India, the response in Russia is lower. No structural breaks were observed in South Africa, Mexico, Argentina and Qatar exchanges during the period considered in study. In addition, it was found that using structural breaks in volatility estimates increased the predictive performance of the models. [ABSTRACT FROM AUTHOR]