scholarly journals Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

2021 ◽  
Author(s):  
Tirngo
Keyword(s):  
Price Volatility ◽  
Bad News ◽  
Good News ◽  
Red Pepper ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Return Volatility ◽  
Agricultural Commodities ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Commodities Prices

Abstract The purpose of this study was to model and forecast volatility of returns for selected agricultural commodities prices using generalized autoregressive conditional heteroskedasticity (GARCH) models in Ethiopia. GARCH family models, specifically GARCH, threshold generalized autoregressive conditional heteroskedasticity (TGARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) were employed to analyze the time varying volatility of selected agricultural commodities prices from 2011to 2021. The data analysis results revealed that, out of the GARCH specifications, TGARCH model with Normal distributional assumption of residuals was a better fit model for the price volatility of Teff and Red Pepper in which their return series reacted differently to the good and the bad news. The study indicated the presence of leverage effect which implied that the bad news could have a larger effect on volatility than the good news of the same magnitude, and the asymmetric term was found to be significant. Also, TGARCH model was found to be the accurate model for forecasting price return volatility of the same commodities, namely Teff and Red Pepper. In short, the study concludes that TGARCH was to be the best fit to model and forecast price return volatility of Teff and Red Pepper in the Ethiopian context.

scholarly journals Price Volatility for Selected Agricultural Commodities in Ethiopia: Evidence from GARCH Models

2021 ◽  
Vol 18 ◽  
pp. 1380-1388
Author(s):  
Tirngo Dinku ◽  
Worku Gardachw ◽  
Ngozi Adeleye
Keyword(s):  
Commodity Prices ◽  
Price Volatility ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Time Varying ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Distributional Assumption ◽  
Egarch Model

This study models the volatility of returns for selected agricultural commodity prices in Ethiopia using the generalized autoregressive conditional heteroskedasticity (GARCH) approach. GARCH family models, specifically threshold GARCH and exponential GARCH were employed to analyze the time varying volatility of selected agricultural commodities prices from 2010 to 2021. The data analysis results revealed that, out of the GARCH specifications, the EGARCH model with the normal distributional assumption of residuals was a better fit model for the price volatility of “teff” and “red pepper” in which their return series reacted differently to the “good” and “bad” news. The study indicated the existence of a leverage effect, which implied that the “bad” news could have a larger effect on volatility than the “good” news of the same magnitude, and the asymmetric term was statistically significant.

scholarly journals ARCH and GARCH Models on the Indonesian Sharia Stock Index

2021 ◽  
Vol 9 (1) ◽  
pp. 27-38
Author(s):  
Budiandru Budiandru ◽  
Keyword(s):  
Profit Sharing ◽  
Stock Index ◽  
Economic Conditions ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Return Volatility ◽  
Previous Period ◽  
Daily Data ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Future

Investments in Islamic stocks are in demand because of the profit-sharing system so that the company is more stable in facing uncertain global economic conditions. This study aims to analyze the volatility of the Indonesian Sharia Stock Index and the Indonesian Sharia Stock Index's potential in the future. We use daily data from 2012 to 2020 and the Autoregressive Conditionally Heteroscedasticity-Generalized Autoregressive Conditional Heteroskedasticity (ARCH-GARCH) method. The results show that the Indonesian Sharia Stock Index's volatility is influenced by the risk of the two previous periods and the return volatility in the previous period. Potential Indonesian Sharia Stock Index tends to fluctuate in return by an average of 3 percent.

Estimating Long-Term Volatility on National Stock Exchange of India

2019 ◽  
pp. 229-237
Keyword(s):  
Stock Exchange ◽  
Stock Index ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Large Sample ◽  
Market Capitalization ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity

The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

Asymmetric statistical features of the Chinese domestic and international gold price fluctuation

2015 ◽  
Vol 29 (17) ◽  
pp. 1550113 ◽  
Author(s):  
Guangxi Cao ◽  
Yingchao Zhao ◽  
Yan Han
Keyword(s):  
Empirical Analysis ◽  
Debt Crisis ◽  
Statistical Tests ◽  
Good News ◽  
Fluctuation Analysis ◽  
Conditional Heteroskedasticity ◽  
Statistical Features ◽  
Gold Market ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Empirical Analysis

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market.

scholarly journals PERAMALAN VOLATILITAS RETURN SAHAM MENGGUNAKAN METODE ASYMMETRIC POWER ARCH (APARCH)

2020 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
JUITA HARYATI SIDADADOLOG ◽  
I WAYAN SUMARJAYA ◽  
NI KETUT TARI TASTRAWATI
Keyword(s):  
Stock Return ◽  
Bad News ◽  
Good News ◽  
Garch Models ◽  
Leverage Effect ◽  
Asymmetric Effect ◽  
Asymmetric Power ◽  
Asymmetric Garch ◽  
Effect Condition

Model APARCH is one of the asymmetric GARCH models. These models are able to capture the incidence of good news and bad news in the volatility. The APARCH model has an asymmetric coefficient to cope with leverage effect by modeling a leverage that has heteroscedasticity and asymmetric effect condition. The results of this research were obtained by the appropriate APARCH model. The model is the APARCH(1,2) model because all parameters are significant. Thus, proceeds from the volatility of stock return for the next 14 days with the model volatility APARCH(1,2) increased from period one to period fourteen.

scholarly journals PENERAPAN MODEL EGARCH PADA ESTIMASI VOLATILITAS HARGA MINYAK KELAPA SAWIT

2015 ◽  
Vol 4 (3) ◽  
pp. 141
Author(s):  
YOSEVA AGUNG PRIHANDINI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Palm Oil ◽  
Financial Analysis ◽  
Price Volatility ◽  
Oil Price ◽  
Bad News ◽  
Good News ◽  
Asymmetric Effect ◽  
Forecasting Models ◽  
Oil Price Volatility ◽  
T Distribution

Good news and bad news (commonly known as the asymmetric effect) on the price of palm oil, has been the grounds of palm oil price volatility. Estimation of volatility needs to be conducted for the purposes of advance financial analysis namely computation of the risk factors, portfolio, futures, etc. In addition, the data of palm oil price is heterscedastical. The heteroscedasticity needs to be overcome in order to generate a sound estimation of volatility. One of the forecasting models for heteroscedastical data and that capable of explaining the good news and bad news over the commodity’s price is the Exponential Autoregressive Conditional Heterocedastic (EGARCH) model.The result of this research, the best of EGARCH models was EGARCH(1,1) with t-distribution. That base of AIC and SIC value.

THE VALUATION OF EXECUTIVE STOCK OPTIONS UNDER GARCH MODELS

2017 ◽  
Vol 32 (3) ◽  
pp. 409-433 ◽  
Author(s):  
Xingchun Wang ◽  
Zhiwei Su ◽  
Guangli Xu
Keyword(s):  
Closed Form ◽  
Stock Price ◽  
Stock Options ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Executive Stock Options ◽  
Time Varying ◽  
Autoregressive Conditional Heteroskedasticity

In this paper, we investigate executive stock options with endogenous departure and time-varying variances. We use a “Generalized Autoregressive Conditional Heteroskedasticity” process to capture the variance process of the log stock price. In addition, we take into consideration the departure risk of the executive and assume that the probability of remaining employed has a power form of stock price ratios. After deriving the closed-form pricing formulae of executive stock options, we illustrate the effects of the departure risk on the values of executive stock options.

scholarly journals GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY MODELING OF ONEYEAR MATURITY GOVERNMENT BONDS OF GREECE DURING SOVEREIGN DEBT CRISIS OF EUROZONE IN 2010

2020 ◽  
Vol 1(13) ◽  
pp. 184-191
Author(s):  
Luka Baryshych ◽  
◽  
Dieudonne Dusengumukiza ◽  
Keyword(s):  
Financial Market ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Local Economy ◽  
Risk Indicators ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Sovereign Debt Crisis ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Of Greece

ination of international trade imbalances, the impact of the global crisis from 2007 to 2012, failure in bailout approaches of European governments that troubled banking industries and private bondholders, high-risk lending and borrowing policies enforced by unrestricted credit requirements during the period from 2002 to 2008 and fiscal policy choices related to government revenues and expenses. The objective is to model the boiling state of the Greek local financial market before the peak of the Sovereign Debt Crisis of Eurozone in 2009, modelling the insights of foreign investors and credit rating organizations. We will identify a set of primary risk factors and their effect on both the local economy and the markets involved to validate the analysis done. In this paper will use both statistical analysis and macroeconomic data modelling techniques to identify a set of primary risk factors or economic variables and their effect on both the local economy of Greece and the markets involved. The selected method of modeling is Generalized autoregressive conditional heteroskedasticity models. The research is based on the data provided by World Bank Data Portal. Results obtained are fitted of 2006-2009 years data Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, forecasting market volatility in 2010 and on. We have discovered, that the Auto Regressive Integrated Moving Average model is not suitable for this problem as there was no notable autocorrelation. The volatility seems to fade out. This observation coincides with reality, as the crisis is about to peak and descend. Systemic risk indicators, primarily used for forecasting state-wide risk, are usually built on insider data of rating agencies or financial institutions. In this paper we obtain results close to Systemic Stress Indicator provided by European Central Bank (ECB) using ARCH and GARCH models on public data. The practical importance is model generation principle, which allows creating a risk indicator based on public financial data. Key words: economy, Single Financial Market, macroeconomic models, commodities prices, risk indicators.

Sign in / Sign up

Export Citation Format

Share Document