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Forecasting Performance Of Asymmetric Garch Stock Market Volatility Models
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Book Synopsis Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models by : Hojin Lee
Download or read book Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models written by Hojin Lee and published by . This book was released on 2017 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH (1,1) model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH (1,1) volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH (1,1) model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.
Book Synopsis Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons by : Turgut Kisinbay
Download or read book Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons written by Turgut Kisinbay and published by International Monetary Fund. This book was released on 2003-06-01 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Book Synopsis Stock Market Volatility by : Greg N. Gregoriou
Download or read book Stock Market Volatility written by Greg N. Gregoriou and published by CRC Press. This book was released on 2009-04-08 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel
Book Synopsis Forecasting Volatility in the Financial Markets by : Stephen Satchell
Download or read book Forecasting Volatility in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2011-02-24 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling
Book Synopsis Forecasting Volatility in the Financial Markets by : John L. Knight
Download or read book Forecasting Volatility in the Financial Markets written by John L. Knight and published by Butterworth-Heinemann. This book was released on 2002 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.
Book Synopsis Volatility Modeling and Forecasting of the Egyptian Stock Market Index Using Arch Models by : Said T. Ebeid
Download or read book Volatility Modeling and Forecasting of the Egyptian Stock Market Index Using Arch Models written by Said T. Ebeid and published by . This book was released on 2020 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper estimates and evaluates the forecasting performance of four alternative ARCH-type Models for predicting stock price index volatility using daily Egyptian data. The competing Models include GARCH, EGARCH, GJR and APAPCH used with four different distributions, Gaussian normal, Student-t, Generalized Error Distribution and skewed Student-t. The estimation results show that the forecasting performance of asymmetric GARCH Models (GJR and APARCH), especially when fat-tailed asymmetric densities are taken into account in the conditional volatility, is better than symmetric GARCH. Moreover, it is found that the APAPCH (1,1) Model Provides the best out-of-sample forecasts among all the candidate Models and the skewed Student-t density is more appropriate for modeling the Egyptian stock market index volatility.
Book Synopsis Modeling and Forecasting of Time-Varying Conditional Volatility of the Indian Stock Market by : Srinivasan Palamalai
Download or read book Modeling and Forecasting of Time-Varying Conditional Volatility of the Indian Stock Market written by Srinivasan Palamalai and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility forecasting is an important area of research in financial markets and immense effort has been expended in improving volatility models since better forecasts translate themselves into better pricing of options and better risk management. In this direction, the present paper attempts to model and forecast the volatility (conditional variance) of the S&P CNX Nifty index returns of Indian stock market, using daily data for the period from January 1, 1996 to January 29, 2010. The forecasting models that are considered in this study range from the simple GARCH(1, 1) model to relatively complex GARCH models, including the Exponential GARCH(1, 1) and Threshold GARCH(1, 1) models. Based on out-of-sample forecasts and a majority of evaluation measures, the results show that the asymmetric GARCH models do perform better in forecasting conditional variance of the Nifty returns rather than the symmetric GARCH model, confirming the presence of leverage effect. The findings are consistent with those of Banerjee and Sarkar (2006) that relatively asymmetric GARCH models are superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.
Book Synopsis The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility by : Linkai Huang
Download or read book The Comparison of Forecasting Performance of Historical Volatility Versus Realized Volatility written by Linkai Huang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: When forecasting stock market volatility with a standard volatility method (GARCH), it is common that the forecast evaluation criteria often suggests that the realized volatility (the sum of squared high-frequency returns) has a better prediction performance compared to the historical volatility (extracted from the close-to-close return). Since many extensions of the GARCH model have been developed, we follow the previous works to compare the historical volatility with many new GARCH family models (i.e., EGARCH, TGARCH, and APARCH model) and realized volatility with the ARMA model. Our analysis is based on the S&P 500 index from August 1st, 2018 to February 1st, 2019 (127 trading days), and the data has been separated into an estimation period (90 trading days) and an evaluation period (37 trading days). In the evaluation period, by taking realized volatility as the proxy of the true volatility, our empirical result shows that the realized volatility with ARMA model provides more accurate predictions, compared to the historical volatility with the GARCH family models.
Book Synopsis Volatility Forecasting and Effects of Asymmetric Patterns in Emerging Markets of Asia by : Kashif Hamid
Download or read book Volatility Forecasting and Effects of Asymmetric Patterns in Emerging Markets of Asia written by Kashif Hamid and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates the performance of linear versus nonlinear methods to predict volatility and effects of asymmetric pattern on the emerging markets of Asia i.e.; China, India, Indonesia Pakistan, Bangladesh and Malaysia. Daily data of stock market returns is taken for the period January 2000 to December 2010. Nonlinear and asymmetric ARCH effects have been observed from the estimations. A range of model from random walk model to multifaceted ARCH class models are used to predict volatility. The results reveal that MA (1) model ranks first with use of RMSE criterion in linear models. For nonlinear models, the ARCH, GARCH (1, 1) model and EGARCH (1, 1) model perform well. GARCH (1,1) model outperforms on the basis of AIC, SIC and Log Likelihood method.
Book Synopsis Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models by : Matteo Manera
Download or read book Forecasting Volatility in European Stock Markets with Non-Linear GARCH Models written by Matteo Manera and published by . This book was released on 2013 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of classical criteria, as well as forecast combination techniques with constant and non-constant weights. With respect to the standard GARCH specification, the non-linear models generally lead to better forecasts in terms of both smaller forecast errors and lower biases. In-sample forecast combination regressions are better than those from single Mincer-Zarnowitz regressions. The out-of-sample performance of combining forecasts is less satisfactory, irrespective of the type of weights adopted.
Book Synopsis The Volatility and Density Prediction Performance of Alternative GARCH Models by : Teng-Hao Huang
Download or read book The Volatility and Density Prediction Performance of Alternative GARCH Models written by Teng-Hao Huang and published by . This book was released on 2019 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewed-t or compound Poisson (jump) distribution based upon a non-linear and asymmetric GARCH (NGARCH) model framework. The empirical results for the Samp;amp;P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the non-normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change.
Book Synopsis A Theoretical Evaluation of the Models for Stock Market Volatility by : Sartaj Hussain
Download or read book A Theoretical Evaluation of the Models for Stock Market Volatility written by Sartaj Hussain and published by . This book was released on 2019 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility forecasting has been widely debated in empirical finance, nevertheless, studies examining issues in volatility and their resolution through various models has received a scant attention. Therefore, the present study which is purely a review work aims to elucidate volatility stylised facts along with discussion on theoretical foundation and procedure of volatility forecasting approaches. To serve this purpose, about sixty research papers were reviewed to extract meaningful insights on stock market volatility and its measurement methods. As a whole, it is observed that unconditional models that are intuitive and simple in estimation ignore most of well-known 'stylised facts' about volatility. GARCH family models though cater to most of volatility stylised facts, yet at the practioners' level, EWMA approach appears to be more reliable and worthwhile. Further, studies show that it is difficult to evaluate GARCH models as empirical results of such a model are dependent on the sampling frequency. Hence, choice among such models remains to be an empirical issue sensitive to length and frequency of data. Finally, GARCH family models expected to take care of main stylised facts like, volatility clustering, asymmetric effect, etc., yet models that have a capacity to handle properties like, non-normal behaviour of stock market volatility are beyond the purview of this study, thus represent a future gap for a literature review based research.
Book Synopsis Asymmetric Volatility in Equity Markets Around the World by : Jone Horpestad
Download or read book Asymmetric Volatility in Equity Markets Around the World written by Jone Horpestad and published by . This book was released on 2018 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: The observation that price declines usually lead to volatility increases is known as the asymmetric volatility effect and has become a stylized fact about the financial markets. We study asymmetric volatility effect in 19 equity indices from North America, Latin America, Europe, Asia and Australia, utilizing not only daily data and four GARCH class models, but also realized volatility calculated from high-frequency data within HAR class models. We first confirm the stylized fact that stock market indices around the world exhibit the asymmetric volatility effect. This effect is stronger for US and European market indices. Second, we find that the asymmetric volatility effect is strong enough to significantly improve out-of-sample forecasts of an accurate HAR volatility model. Third, we show that forecast improvements of the asymmetric volatility models are largest during periods of higher market volatility, when accurate volatility forecasts matter the most.
Book Synopsis Application of GARCH Models for Modeling Stock Market Volatility by : Shabarisha N.
Download or read book Application of GARCH Models for Modeling Stock Market Volatility written by Shabarisha N. and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Return is the major attribute of an investment asset which can be construed as a random variable, and the 'variability in return' can be interpreted as volatility. Forecasting volatility and modeling it are the most prolific areas for research. This paper empirically investigates the conditional variance (volatility) pattern in Indian stock market based on financial time series data that consists of daily closing prices of CNX Nifty 50 market index for 10 years from April 2006 to March 2016. For the purpose of estimating conditional variance (volatility) in the daily returns of the index, Autoregressive Conditional Heteroskedasticity (ARCH) models are employed. Both symmetric and asymmetric models are used to capture stylized facts about CNX Nifty 50 market index returns such as volatility clustering and leverage effect. The findings of the study show that the asymmetric models are a better fit than symmetric models, confirming the presence of volatility clustering and leverage effect.
Book Synopsis Forecasting Stock Market Volatility Using (nonlinear) Garch Models by : Philip Hans Franses
Download or read book Forecasting Stock Market Volatility Using (nonlinear) Garch Models written by Philip Hans Franses and published by . This book was released on 1995 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modelling the Asymmetry of Stock Market Volatility by : Olan Henry
Download or read book Modelling the Asymmetry of Stock Market Volatility written by Olan Henry and published by . This book was released on 1995 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Realized Range Volatility Forecasting by : Massimiliano Caporin
Download or read book Realized Range Volatility Forecasting written by Massimiliano Caporin and published by . This book was released on 2013 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we estimate, model and forecast Realized Range Volatility, a realized measure and estimator of the quadratic variation of financial prices. This quantity was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We consider the impact of the microstructure noise in high frequency data and correct our estimations, following a known procedure. Then, we model the Realized Range accounting for the well-known stylized effects present in financial data and we investigate the role that macroeconomic and financial variables play when forecasting daily stocks volatility. We consider an HAR model with asymmetric effects with respect to the volatility and the return, and GARCH and GJR specifications for the variance equation. Moreover, we consider a non Gaussian distribution for the innovations. Finally, we extend the model including macroeconomic and financial variables that capture present and the future state of the economy. We find that these variables are significantly correlated with the first common component of the volatility series and they have a highly in-sample explanatory power. The analysis of the forecast performance in 16 NYSE stocks suggests that the introduction of asymmetric effects with respect to the returns and the volatility in the HAR model result in significant improvement in the point forecasting accuracy as well and the variables related with the U.S stock market performance and proxies for the credit risk.
