scholarly journals Using Models of the GARCH Family to Estimate the Level of Food and Non-Food Inflation in Ethiopia

2021 ◽  
Vol 20 (4) ◽  
pp. 726-749
Author(s):  
T.H. Abebe ◽  
Keyword(s):  
Financial Time Series ◽  
Garch Models ◽  
Suitable Model ◽  
Volatility Clustering ◽  
Inflation Volatility ◽  
Volatility Model ◽  
Asymmetric Garch ◽  
Alternative Hypotheses ◽  
Food Inflation ◽  
Family Models

An increase in inflation volatility implies higher uncertainty about future prices. As a result, producers and consumers can be affected by the increased inflation volatility, because it increases the uncertainty and the risk in the market. Thus, inflation volatility attracts the attention of researchers to find a suitable model which can predict the future conditions of the market. This study aims to fit appropriate ARMA-GARCH family models for food and non-food inflation rate of from the period January 1971 through June 2020. Since the main objective of the study is identifying an appropriate model for inflation series, the null and alternative hypotheses are defined in comparison of the two types of models. H0: The symmetric GARCH models better capture inflation volatility of Ethiopia. H1: The asymmetric GARCH models better capture inflation volatility of Ethiopia. The ARMA-GARCH family models were applied to capture the stylized facts of financial time series such us leptokurtic, volatility clustering and leverage effects. The mean model results show that, an ARMA (1, 2) and ARIMA (0, 1, 1) models are identified as the best fitted model for food and non-food inflation, respectively. From the estimation results of volatility model, an asymmetric TGARCH (1, 1) model with Student's t- distributional assumptions of the residual is the best model for non-food inflation. Thus, modeling of information, news of events is very significant determinants of volatility and GARCH family models are appropriate for the given series (monthly food-inflation volatility) of Ethiopia under the study period considered.

scholarly journals Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Amare Wubishet Ayele ◽  
Emmanuel Gabreyohannes ◽  
Hayimro Edmealem
Keyword(s):  
Inflation Rate ◽  
Empirical Investigation ◽  
Financial Time Series ◽  
Supply And Demand ◽  
Price Volatility ◽  
Market Liquidity ◽  
Volatility Model ◽  
Portfolio Managers ◽  
Using Data ◽  
Family Models

Like most commodities, the price of silver is driven by supply and demand speculation, which makes the price of silver notoriously volatile due to the smaller market, lower market liquidity, and fluctuations in demand between industrial and store value use. The concern of this article was to model and forecast the silver price volatility dynamics on the Ethiopian market using GARCH family models using data from January 1998 to January 2014. The price return series of silver shows the characteristics of financial time series such as leptokurtic distributions and thus can suitably be modeled using GARCH family models. An empirical investigation was conducted to model price volatility using GARCH family models. Among the GARCH family models considered in this study, ARMA (1, 3)-EGARCH (3, 2) model with the normal distributional assumption of residuals was found to be a better fit for price volatility of silver. Among the exogenous variables considered in this study, saving interest rate and general inflation rate have a statistically significant effect on monthly silver price volatility. In the EGARCH (3, 2) volatility model, the asymmetric term was found to be positive and significant. This is an indication that the unanticipated price increase had a greater impact on price volatility than the unanticipated price decrease in silver. Then, concerned stockholders such as portfolio managers, planners, bankers, and investors should intervene and pay due attention to these factors in the formulation of financial and related market policy.

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 PERBANDINGAN MODEL GARCH SIMETRIS DAN ASIMETRIS PADA DATA KURS HARIAN

2020 ◽  
Vol 4 (4) ◽  
pp. 627-637
Author(s):  
Isna Shofia Mubarokah ◽  
Anwar Fitrianto ◽  
Farit M Affendi
Keyword(s):  
Exchange Rates ◽  
Garch Model ◽  
Financial Data ◽  
Garch Models ◽  
Data Series ◽  
Asymmetric Effect ◽  
Daily Return ◽  
Volatility Model ◽  
Asymmetric Garch ◽  
The Garch Model

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

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 Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models

2019 ◽  
Vol 13 (3) ◽  
pp. 1087
Author(s):  
Ezatul Akma Abdullah ◽  
Siti Meriam Zahari ◽  
S.Sarifah Radiah Shariff ◽  
Muhammad Asmu’i Abdul Rahim
Keyword(s):  
Stock Returns ◽  
Price Index ◽  
Financial Time Series ◽  
Composite Index ◽  
Garch Models ◽  
Financial Assets ◽  
Kuala Lumpur ◽  
Financial Decisions ◽  
Auto Regressive ◽  
Public Holidays

It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models.  Thus, the aim of this study is to model the KLCI stock market using two models; Stochastic Volatility (SV) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH). This study employs an SV model with Bayesian approach and Markov Chain Monte Carlo (MCMC) sampler; and GARCH model with MLE estimator. The best model will be used to forecast the future volatility of stock returns. The study involves 971 daily observations of KLCI Closing price index, from 2 January 2008 to 10 November 2016, excluding public holidays. SV model is found to be the best based on the lowest RMSE and MAE values.

Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model

2019 ◽  
pp. 259-268
Keyword(s):  
Time Series ◽  
Banking Sector ◽  
Stock Exchange ◽  
Time Lag ◽  
Garch Model ◽  
Recent Past ◽  
Volatility Clustering ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Asymmetric Garch

The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.

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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