scholarly journals Estimating and Forecasting Volatility of Financial Markets Using Asymmetric GARCH Models: An Application on Turkish Financial Markets

2014 ◽  
Vol 6 (4) ◽  
Author(s):  
Rasim Ilker Gokbulut ◽  
Mehmet Pekkaya
Keyword(s):  
Financial Markets ◽  
Garch Models ◽  
Asymmetric Garch ◽  
Forecasting Volatility

scholarly journals Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models

2007 ◽  
Vol 12 (2) ◽  
pp. 115-149
Author(s):  
G.R. Pasha ◽  
Tahira Qasim ◽  
Muhammad Aslam
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Garch Models ◽  
Financial Time ◽  
Daily Data ◽  
Asymmetric Garch ◽  
Gjr Model ◽  
Forecasting Volatility ◽  
Tail Distributions

In this paper we compare the performance of different GARCH models such as GARCH, EGARCH, GJR and APARCH models, to characterize and forecast financial time series volatility in Pakistan. The comparison is carried out by comparing symmetric and asymmetric GARCH models with normal and fat-tailed distributions for the innovations, over short and long forecast horizons. The forecasts are evaluated according to a set of statistical loss functions. Daily data on the Karachi Stock Exchange (KSE) 100 index are analyzed. The empirical results demonstrate that the use of asymmetry in the GARCH models and the assumption of fat-tail distributions for the innovations improve the volatility forecasts. Overall, EGARCH fits the best while the GJR model, with both normal and non-normal innovations, seems to provide superior forecasting ability over short and long horizons.

scholarly journals INVESTIGATING ABNORMAL VOLATILITY TRANSMISSION PATTERNS BETWEEN EMERGING AND DEVELOPED STOCK MARKETS: A CASE STUDY

2020 ◽  
Vol 21 (6) ◽  
pp. 1561-1592
Author(s):  
Cristi Spulbar ◽  
Jatin Trivedi ◽  
Ramona Birau
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Financial Integration ◽  
Global Financial Crisis ◽  
Spillover Effects ◽  
Garch Models ◽  
Asymmetric Garch ◽  
Developed Stock Markets ◽  
The Impact

The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

scholarly journals Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Marcin Fałdziński ◽  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Absolute Error ◽  
Futures Contracts ◽  
Support Vector ◽  
Garch Models ◽  
Forecasting Performance ◽  
Asymmetric Garch ◽  
Heating Oil ◽  
Forecasting Volatility ◽  
Forecasting Errors

We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

Learning for infinitely divisible GARCH models in option pricing

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fumin Zhu ◽  
Michele Leonardo Bianchi ◽  
Young Shin Kim ◽  
Frank J. Fabozzi ◽  
Hengyu Wu
Keyword(s):  
Option Pricing ◽  
Stock Returns ◽  
Bayesian Learning ◽  
Space Structure ◽  
Estimation Methods ◽  
Garch Models ◽  
Learning Approaches ◽  
Infinitely Divisible ◽  
Asymmetric Garch ◽  
Non Gaussian

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

scholarly journals An application of multivariate GARCH models in research for the interaction of world financial markets

Equilibrium ◽  
2009 ◽  
Vol 2 (1) ◽  
pp. 61-68
Author(s):  
Tomasz Chruściński
Keyword(s):  
Financial Markets ◽  
Stock Markets ◽  
Multivariate Garch ◽  
Garch Models ◽  
Data Sets ◽  
Conditional Correlation ◽  
Main Emphasis ◽  
Multivariate Garch Models ◽  
Diagonal Vech

This article presents information about taxonometric methods in classification stock-markets and selected Multivariate GARCH models. The main emphasis is placed on which market (country) influences others. Research has been geared towards three kinds of measurement: diagonal VECH models, diagonal BEKK models and Constant Conditional Correlation. The results obtained for the DBEKK model is optimal for most data-sets.

Re-Examining the Nifty Returns after the First Decade of Derivative Trading in Indian Capital Market Using Non-Linear Asymmetric GARCH Models

2012 ◽  
Vol 3 (4) ◽  
pp. 29-52 ◽  
Author(s):  
Sunita Narang
Keyword(s):  
Capital Market ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Indian Market ◽  
Total Period ◽  
Dummy Variable ◽  
Asymmetric Garch ◽  
Indian Stock Market ◽  
Surrogate Index ◽  
The Impact

This article examines the Indian stock market for conditional volatility using symmetric and asymmetric GARCH (Generalized Autoregressive Conditional Heteroskedasticity) variants with reference to a comprehensive period of 20 years from July 3, 1990 to November 30, 2010 using S&P CNX Nifty. The impact of future trading on Nifty return and volatility is assessed using dummy variable in total period and using Log (Open Interest of Nifty futures) in post-derivative period. Along with the period of two decades the analysis has also been done on a sub-period of a decade from 1995 to 2005 with NiftyJunior as surrogate index as it had no derivatives during this period. The results show that the PGARCH model is best suited to Indian market conditions.

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