scholarly journals Price volatility of staple food using ARCH-GARCH model

2021 ◽  
Vol 653 (1) ◽  
pp. 012146
Author(s):  
I Setiawati ◽  
Ardiansyah ◽  
R Taufikurohman
Keyword(s):  
Garch Model ◽  
Price Volatility ◽  
Staple Food

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

JUMP, NON-NORMAL ERROR DISTRIBUTION AND STOCK PRICE VOLATILITY — A NONPARAMETRIC SPECIFICATION TEST

2009 ◽  
Vol 54 (01) ◽  
pp. 101-121
Author(s):  
MOHAMMAD MASUDUR RAHMAN ◽  
LAILA ARJUMAN ARA ◽  
ZHENLONG ZHENG
Keyword(s):  
Stock Price ◽  
Stock Return ◽  
Garch Model ◽  
Price Volatility ◽  
Stock Index ◽  
Specification Test ◽  
Significance Level ◽  
Student T Distribution ◽  
T Distribution ◽  
Distribution Assumption

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student t-distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from 20 November 1999 to 9 October 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 November 2001. The model introducing GARCH jump effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

Food price volatility and socio-inequalities in household food insecurity during coronavirus disease-2019 lockdown in Nansana municipality, Wakiso district Uganda

2020 ◽  
Author(s):  
Edward Buzigi ◽  
Stephen Onakuse
Keyword(s):  
Food Insecurity ◽  
Virus Disease ◽  
Multivariate Logistic Regression Analysis ◽  
Price Volatility ◽  
Food Prices ◽  
Food Price ◽  
Food Aid ◽  
Household Food Insecurity ◽  
Staple Food ◽  
Household Food

Abstract BackgroundThis study assessed staple food price volatility, food consumption scores (FCS) and prevalence of household food insecurity (HHFI) and its socio-inequalities during enforcing and lifting corona virus disease -2019 (COVID-19) lockdown in Nansana municipality, Uganda.MethodsA repeated households (HHs) based cross-sectional study was conducted in urban Nansana Municipality, Uganda. A total of 405 HHs (205 slum and 200 non-slum) were selected using stratified random sampling. Data on social demographics and FCS in the previous 7 days were collected using questionnaire-based telephone interviews with HH heads. Prices for staple foods was collected by asking food sellers from local markets. Mean staple food price differences between before COVID-19 lockdown and during enforcing or lifting the lockdown was tested by paired t test. A binary outcome of HHFI (FCS of 0-35) and food secure (FCS>35) HHs was created. The association between exposure variables and HHFI was tested using multivariate logistic regression analysis at a probability value of 5%.ResultsMean price of staple food significantly increased between before and during enforcing the COVID-19 lockdown (p <0.0001). Mean FCS during COVID-19 lockdown were at borderline for either slum (22.8) or non-slum (22.9) HHs, and were not significantly different from each other (p=0.06). During partial lifting of the lockdown, FCS among slum HHs were significantly lower at 20.1 (poor) compared to non-slum HHs at 22.7 (borderline) (p=0.01). The mean FCS was significantly higher at borderline (24.5) among HHs that received food aid compared to poor FCS (18.2) among slum HHs that did not receive food aid (p<0.0001). The prevalence of HHFI was high and not significantly different (p>0.05) between slum (94.6%) and non-slum (93.5%) HHs during COVID-19 lockdown. HHFI was higher in slum (98.5%) than non-slum (94%) HHs (p<0.05) on partial lifting of the lockdown. Adjusted odds ratio (AOR) showed that being a wage earner and employed HH head was positively (AOR: 8.3, 95% CI: 1.9-36.2) and negatively (AOR: 0.07, CI: 0.02-0.2) associated with HHFI, respectively. During partial lifting of COVID-19 lockdown, slum HHs (AOR: 11.8, 95% CI: 1.5-91.3), female headed HHs (AOR: 11.9, 95%CI: 1.5-92.7), wage earners (AOR: 10.7, 95% CI: 1.4-82.9) and tenants (AOR: 4.0, 95% CI: 1.1- 14.7) were positively associated with HHFI. Conclusion Staple food prices increased during enforcing COVID-19 lockdown compared before lockdown. Food aid distribution during COVID-19 lockdown improved FCS among slum HHs, however, it did not prevent against slum HHFI.

scholarly journals INTEREST RATES AND THE VOLATILITY AND CORRELATION OF COMMODITY PRICES

2016 ◽  
Vol 22 (3) ◽  
pp. 600-619 ◽  
Author(s):  
Joseph W. Gruber ◽  
Robert J. Vigfusson
Keyword(s):  
Interest Rates ◽  
Commodity Prices ◽  
Garch Model ◽  
Price Volatility ◽  
Inventory Costs ◽  
Common Shocks ◽  
The Past ◽  
Transitory Shocks ◽  
Idiosyncratic Shocks ◽  
Panel Garch

We propose a novel explanation for the observed increase in the correlation of commodity prices over the past decade. In contrast to theories that rely on the increased influence of financial speculators, we examine the effect of interest rates on the volatility and correlation of commodity prices via a panel GARCH model. In theory, lower interest rates decrease the volatility of prices, as lower inventory costs promote the smoothing of transient shocks, and increase price correlation if common shocks are more persistent than idiosyncratic shocks. Empirically, we find that price volatility attributable to transitory shocks declines with interest rates, whereas particularly for metals prices, price correlation increases as interest rates decline.

scholarly journals Stock price volatility during the COVID-19 pandemic: The GARCH model

2021 ◽  
Vol 18 (4) ◽  
pp. 12-20
Author(s):  
Endri Endri ◽  
Widya Aipama ◽  
A. Razak ◽  
Laynita Sari ◽  
Renil Septiano
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Practical Implication ◽  
Abnormal Returns ◽  
Garch Model ◽  
Price Volatility ◽  
Efficient Market Hypothesis ◽  
Empirical Literature ◽  
Stock Price Volatility ◽  
The Garch Model

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

scholarly journals Spatial Differences in Rice Price Volatility: A Case Study of Pakistan 1994–2011

2017 ◽  
Vol 56 (3) ◽  
pp. 265-289
Author(s):  
Burhan Ahmad ◽  
Ole Gjølberg ◽  
Mubashir Mehdi
Keyword(s):  
Garch Model ◽  
Price Volatility ◽  
Positive Association ◽  
Jel Classification ◽  
Volatility Clustering ◽  
Lm Tests ◽  
Regional Markets ◽  
Spatial Differences ◽  
Conditional Correlations

Prices of agricultural commodities tend to be more volatile in comparison to other commodities. Volatility can result in inefficient allocation of the resources by the farmers, traders and consumers. Rice is the second major staple and export item of Pakistan. This study presents the trends in volatility of regional rice markets of Pakistan and analyses spatial differences in volatility across regional rice markets in Pakistan from 1994 to 2011, and also draws comparison of volatility with the international market. ARCH-LM tests are applied to check the presence of volatility and volatility clustering is found in all the markets. Tests for equality of variance and dynamic conditional correlations (DCC) GARCH model are employed to analyse the spatial differences across the regional rice markets of Pakistan. The results indicate the presence of spatial differences in volatility. Positive conditional correlations in the dynamic conditional correlations (DCC) GARCH model are found which indicate positive association of volatility across markets. Spatial differences in volatility and its persistence reflect the differences in market forces, infrastructure and information flow which leads to varying degree of risk across markets and some regions are exposed to higher risk. The study found out that Hyderabad and Sukkur are the most volatile markets and their volatility levels are highly persistent and require highest time to return to its long-term mean which makes them the riskiest rice markets. Investments in infrastructure, particularly in transportation and controlling the market power of middlemen may reduce price risk across markets particularly in the most risky markets. JEL Classification: C22, C32, Q11, Q13, Q18 Keywords: Rice Prices Volatility, Regional Markets, Pakistan. DCC-GARCH-models

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