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 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.

An Empirical Investigation on Iron-Ore Price volatility with special reference to Dindigul Lock makers

2016 ◽  
Vol 3 (01) ◽  
Author(s):  
S. Gurusamy ◽  
P. Hemavathy ◽  
V. Leelavathy
Keyword(s):  
State Government ◽  
Iron Ore ◽  
Empirical Investigation ◽  
Raw Materials ◽  
Price Volatility ◽  
Raw Material ◽  
Indian Market ◽  
Volatility Model ◽  
High Volatility ◽  
The Cost

Iron-ore is the staple raw material required for manufacturing locks. Iron ore price contributes to the cost of raw material for Dindigul lock makers. High volatility has been widely observed in the prices of Indian iron ore market. Irrefutably, the Lock maker's optimism has been battered by volatility and thus critically has an effect on their economic growth. This study develops and examines the conditional volatility model in an attempt to confine the prominent features of volatility in iron ore prices prevailing in Indian market confining to the benefit of Dindigul Lock makers. Results of the study exhibit that the high volatility of iron ore price is expected when the negative shocks are present. The Centre and State government are accountable in steering rural development through adequate disbursement of credit in the form of incentives to the lock makers to buy raw materials at an affordable cost.

Solar Renewable Energy Credit Price Volatility and Investment Returns: Have Policies Stranded Capital Investment?

Commonwealth ◽  
2017 ◽  
Vol 19 (1) ◽  
Author(s):  
Somayeh Youssefi ◽  
Patrick L. Gurian
Keyword(s):  
Solar Energy ◽  
Capital Investment ◽  
Supply And Demand ◽  
Price Volatility ◽  
The United States ◽  
Investment Model ◽  
Capital Investments ◽  
Demand Model ◽  
Market Prices ◽  
Investment Returns

Pennsylvania is one of a number of U.S. states that provide incentives for the generation of electricity by solar energy through Solar Renewal Energy Credits (SRECs). This article develops a return on investment model for solar energy generation in the PJM (mid-­Atlantic) region of the United States. Model results indicate that SREC values of roughly $150 are needed for residential scale systems to break even over a 25-­year project period at 3% interest. Market prices for SRECs in Pennsylvania have been well below this range from late 2011 through the first half of 2016, indicating that previous capital investments in solar generation have been stranded as a result of steep declines in the value of SRECs. A simple conceptual supply and demand model is developed to explain the sharp decline in market prices for SRECs. Also discussed is a possible policy remedy that would add unsold SRECs in a given year to the SREC quota for the subsequent year.

scholarly journals Minnesota—too late for a Sovereign Wealth Fund?

2021 ◽  
Author(s):  
Christopher R. McIntosh ◽  
Neil A. Wilmot ◽  
Adrienne Dinneen ◽  
Jason F. Shogren
Keyword(s):  
Renewable Resources ◽  
Iron Ore ◽  
Inflation Rate ◽  
Supply And Demand ◽  
Sovereign Wealth Funds ◽  
Funding Mechanism ◽  
Investment Return ◽  
Investment Returns ◽  
Fund Size ◽  
Forward Looking

AbstractTen states have created natural-resource-based Sovereign Wealth Funds (SWF) to allow a fraction of the wealth derived from the extraction of non-renewable resources to be available for future use. Minnesota does not have a SWF, even though companies have been mining in the state for over 100 years. Herein, we present backward and forward-looking scenarios to estimate the potential magnitude of a “what-if” extraction-based fund. A 1.5% of value tax is suggested as an SWF funding mechanism. Based on historical extraction, prices, and investment returns, a large SWF could already exist. In the forward-looking section, we begin by econometrically estimating the supply and demand of US iron ore production to better understand how an increase in mining taxes would likely effect mining output (i.e., the production effect). After accounting for an estimated 4% production loss, results suggest enough minerals could still be extracted to create a permanent fund with between $930 million (US) and $1.6 billion dollars (US) in direct contributions by 2050 (depending on price). Using reasonable assumptions of a 2% inflation rate and a 5% annual investment return, the fund size could range from $3 billion to $5 billion by 2050.

Algorithmic Trading and the Market for Liquidity

2013 ◽  
Vol 48 (4) ◽  
pp. 1001-1024 ◽  
Author(s):  
Terrence Hendershott ◽  
Ryan Riordan
Keyword(s):  
Supply And Demand ◽  
Market Liquidity ◽  
Algorithmic Trading ◽  
Limit Order ◽  
Market Order

AbstractWe examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.

An Agent-Based Model to Forecast the Inflation Rate in the Eurozone

2017 ◽  
pp. 72-92
Author(s):  
Juan Luis Santos ◽  
Jagoda Anna Kaszowska ◽  
Tomás Mancha Navarro
Keyword(s):  
Financial Institutions ◽  
Inflation Rate ◽  
Phillips Curve ◽  
Supply And Demand ◽  
Single Agent ◽  
Market Equilibrium ◽  
Agent Based Model ◽  
Agent Based ◽  
Long Run ◽  
Inflation Rates

The aim of the agent-based model presented in this chapter is to explain the determinants of inflation and to forecast the inflation rate in the Eurozone for the next five years. The behaviors of agents and their expectations are interrelated and explained by macroeconomic models applied to heterogeneous agents of three classes: individuals, companies and financial institutions. In addition, the behavior of public sector and central bank is also modeled with a single agent of each kind. Once the quantitative easing policy is implemented, the quantitative theory of money expects higher inflation rates in the long run. Inflation should remain low taking into account the Phillips-Curve. Last, according to the Aggregated Supply and Demand as well as to the Money Market equilibrium, the behaviors modeled allow forecasting low inflation. However, an external shock, as it would be an increase in the price of important commodities, can alter the inflation rate to a great extent.

scholarly journals Dynamic Model of a Sustainable Water Resources Utilization System with Coupled Water Quality and Quantity in Tianjin City

2020 ◽  
Vol 12 (10) ◽  
pp. 4254
Author(s):  
Yutong Tian ◽  
Chunhui Li ◽  
Yujun Yi ◽  
Xuan Wang ◽  
Anping Shu
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Water Supply ◽  
Supply And Demand ◽  
Sustainable Use ◽  
Social Economy ◽  
Using Data ◽  
Water Quality And Quantity ◽  
Sustainable Water Resources ◽  
Water Resources Utilization

With the development of industrial and agricultural production and the social economy, the demand for water resources has gradually increased. In this paper, based on the principles of system dynamics, a sustainable water resources utilization model with coupled water quality and quantity is established using STELLA software to assess the sustainable use of water resources. The model includes two modules: a water supply module and a water quality module. The water supply module includes four sub-systems: economy, population, water supply, and water demand. The water quality module consists of an environmental sub-system. The model is suitable for Tianjin, where water resources are scarce. Calibration is performed using data from 2013–2016, and verification is performed using data from 2017. The simulation results are good. In order to compare the sustainable use of water resources in different development scenarios in Tianjin for 2025, a sensitivity analysis is performed for each variable, and four decision variables are selected to establish four water resources use scenarios (Scenarios 1–4). The results show that, compared with scenario 1, water shortages in scenarios 2 and 3 are delayed. Scenario 4, with stable economic growth and environmental consideration, can effectively resolve the contradiction between water supply and demand in the future, and is more conducive to the improvement of water quality. Finally, based on the above research, measures to solve water resources problems are proposed, in order to provide a reference for the sustainable use of water resources and optimization of water resources allocation in Tianjin.

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