In Greece, domestic pork production in 2005 contributed about 28% of the total meat pro- duction and satisfied about 40% of the domestic demand, while imports mainly from other EU countries were needed to satisfy the local con- sumption. Up to the 1950s, pig production in Greece was related to self consumption. The pork industry started its evolution during the 1960s and was characterized mainly by small or me- dium sized farms. Advances in production tech- nology and changes in consumer preferences were credited as the driving forces behind the rapid evolution of the pork industry in Greece over the last three decades. The Greek pork in- dustry is mainly composed of small sized enter- prises, but recently there has been a significant increase in large sized, vertically integrated en- terprises with updated technology. It should be noted, however, that during the last 15 years, a decline in pork production has occurred, even though a transformation toward modern pro- duction methods and organized enterprises has taken place in the Greek pig industry. For ex- ample, since the beginning of the 1990s, pork production declined from about 150 thousand tons to about 129 thousand tons in 2005. During the aforementioned period, there was a continu- ous increase of domestic pork demand, which was satisfied by increasing imports which were accounted about 60% of the domestic pork con- sumption in 2005. Imports were mainly from EU countries, that is, The Netherlands, Germany, and France. The removal of trade barriers within the EU by 1993 increased competition from other EU countries for Greek pork producers. This caused a decrease in the Greek pork production since the Greek pig sector operated with higher production costs and lower productivity, com- pared with the other competing EU producers. Furthermore, after the last reform of the Common Agricultural Policy (CAP), which took place in 2003, the EU is increasingly being directed
response model the price of milk should also be taken into account. In Greece, cattle are usually used for both meat and milk production and in that case milk and meat behave like competitive products. A high milk price can have a negative effect on beef supplied quantity mainly because producers decide to market milk rather than to use it as a feed for the young calves. Therefore, a high milk price induces producers to slaughter faster young calves in lower weight. Also, if producers believe that milk price will continue to stay high in the future they will probably decide not to slaughter some young females. Instead they will use them to increase the size of the breeding stock increasing thus future milk production.
In this study, we assume that prices follow an autoregressive process, and an asymmetric generalized autoregressive conditional heteroskedasticity (Asymmetric GARCH) process is adapted to model the pricevolatility. This technique is appropriate when modeling agricultural price volatilities because it allows unconditional variance to vary over time. Furthermore, modelling price volatilities by the Asymmetric GARCH model, allows us to investigate the possible asymmetric effects of price shocks. The possible existence of asymmetry of corn pricevolatility can provide useful information about market structure. The rest of the paper is structured as follows. The second section presents the econometric model of corn production and data. Then the empirical results are explained, and the final section presents the implications and conclusions of the study.
This paper examines the supplyresponse for four meat categories, i.e. beef, broiler, lamb, and pork, in Greece. A multivariate GARCH model with Cholesky decomposition is used to incorporate pricevolatility into the rational expectations supplyresponse model for each meat category, providing that the conditional covariance matrix remains positive definite without imposing any restrictions on the parameters. The empirical results confirm the existence of rational behaviour by meat producers in all the meat categories and pricevolatility is found to have a significant negative effect on the production level, denoting that producers are risk averse, with broiler production presenting the highest volatility effect, i.e.
As a result, researchers put extra weight on this crucial part of their study trying to reveal the models which are more suitable and effective in modelling and making safe forecasts relative to a particular energy commodity’s price and returns, or a portfolio of investments related to energy products. Several procedures and tests have been developed to deal with this issue, however the most popular among researchers in the field of energy economics are the direct evaluation of the models’ results based on one or a number of approved loss functions for the particular type of study, as well as comparing the examined models relative to a predefined benchmark model for a predetermined loss function like in the Superior Predictive Accuracy (SPA) test of Hansen (2005) and Koopman et al. (2005) and Diebold and Mariano (1995) DM test. The benchmark used in these tests is usually one of the investigated models, with all of the models eventually being tested as benchmarks one by one. 126.96.36.199 Using loss functions to evaluate forecasting performance
3.4.1. Lag order selection for conditional variance model The Q-test statistics of the squared residuals of the mean model in Appendix Table 4 suggests that all spikes are significant but it is better to consider lag order ≤ 3 because higher lag orders may have lower effects than lower lag orders . From the given values of AIC and BIC we can see that GARCH (1,1) with normal distribution, GARCH (1,1) with GED distribution, GARCH (1,1) with t- distribution, EGARCH (1,1) with GED distribution, EGARCH (1,1) with normal distribution and EGARCH (1,1) with t-distributional assumption are selected as appropriate models having small information criteria value. The next thing we do after selecting candidate models having different distributional assumptions is measuring their forecasting abilities using MSE (mean square error), RMSE (root of mean square error) and U thel’s inequality coefficient. The best model among the candidate models used for estimating and forecasting the volatility in return series of price inflation in Ethiopia.
This study set out to investigate the effect of oil pricevolatility on stock price in Nigeria using quarterly data from 1990 to 2012 period. It is well known that oil prices have exerted significant impacts on most macroeconomic variables in Nigeria (see Adegboye, 2013 and Akpan, 2009). This is the motivation for the current study. Given the pattern of the relationships, a dynamic framework was devised for the study. Hence, both statistical and econometric techniques were used for the analysis. Moreover, we argue that it is volatility in oil prices, not the levels that affect the stock prices. Thus, both the Error Correction Mechanism (ECM) and the Bi-variate GARCH model were used to test for the relationships and to check if volatility in oil prices are transmitted to stock prices in Nigeria. Based on the empirical analysis, we find that oil pricevolatility stimulates stock price changes in Nigeria. Moreover, oil pricevolatility generates and stimulates stock prices volatility in Nigeria. The basic indication of the modeling technique used in the study shows that volatility in oil prices are transmitted to stock pricevolatility.
for the remaining cereal crops under consideration viz., maize and bajra (Table 7). Out of the aforementioned crops, only maize crop coefficient of lagged price of itself was significant (negative signed). The short run elasticity revealed acreage responsiveness of a crop to price changes in preceding crop period, and the elasticity for these crops ranged from -0.21 to 0.094: negative priceresponse was observed in maize and bajra (non- commercial crops). However, it should be noted that negative supplyresponse is not an uncommon feature on supplyresponse as seen in earlier studies: Sud and Kahlon (1969) observed negative price coefficients in nearly six gram cultivating districts in Punjab; Cumming (1975) also observed negative price coefficient in nearly half of the 100 wheat cultivating districts in India; Jhala (1979) also observed negative priceresponse in six out of fourteen cases he studied on groundnut crop. In studies of Rao and Krishna (1965); Krishna and Rao (1967) and Bhowmick and Goswami (1998), this kind of conflicting estimates were reported. The long run elasticity reflects the acreage responsiveness of a crop to price change given sufficient time for adjustment. None of the crop under consideration showed very high long-run elasticity as such the impact of price policy on these crops would be mild/light in the long-run. The number of years required for price effect to materialize depends on the technological and institutional constraints faced by the farmers for a particular crop. The higher the constraints, the more is the time required for adjustment. It was observed that Jowar and maize crops, respectively, took medium time for adjustment, while barley and wheat crops, respectively; take very small time for adjustment. The smaller the time for adjustment, the more effective is the price policy instruments in bringing desired change in the supply of a crop. In the case of bajra, the number of years required for the price to materialize was indeterminate.
Abstrakt: Článek se zabývá analýzou cenové transmise v zemědělsko-potravinářské vertikále vepřového masa. Analýza se zaměřuje na určení typu tržní struktury ve vertikále s využitím odvozeného teoretického modelu a odhadnutého re- dukovaného modelu cenové transmise ve formě VEcM. impulse-response analýza a dekompozice rozptylu odhadnutého VEcM dále ukazuje reakci systému na inovace a interakci mezi proměnnými v delším prognostickém horizontu. Výsledky analýzy naznačují, že zpracovatelé ve vztahu k zemědělcům využívají své síly, tj. tržní strukturu lze v této části vertikály označit jako oligopson. impulse-response analýza ukazuje, že systém se vrací poměrně rychle do rovnovážného stavu a reakce na inovace jsou pozitivní. Dekompozice rozptylu dále informuje, že s rostoucím prognostickým horizontem roste role ceny potravinářských výrobců ve vysvětlení chyby prognózy cen zemědělských výrobců. z obdržených výsledků plyne, že zemědělsko-potravinářská vertikála vepřového masa má charakter poptávkově řízené vertikály. Typ tržní struktury dále implikuje, že podpora zemědělců je v tomto případě sdílena ostatními články vertikály, což snižuje její efektivnost. Klíčová slova: cenová transmise, vepřové maso, zemědělsko-potravinářská vertikála, tržní struktura, zemědělec, potravi- nářský průmysl
One way to achieve this goal is to increase tax penalty rates. An increase in the tax penalty rate will not guarantee though positive results for the policy makers, because some occupations are characterized by high opportunities for tax evasion and thus taxpayers can hide their income more easily, because either tax audits may be less frequent or unreported income may be difficult to detect. Besides adjusting tax penalty rates, policy makers could potentially combat tax evasion by raising the probability of tax audits too. In the context of the Greek economy, it was found that higher probabilities of tax audits on risk-averse firms may be a more effective method to deter tax evasion than raising tax penalties (Goumagias, Hristu-Varsakelis, 2012). However, increasing the frequency of tax audits may be costly for the government as policy makers would spend more resources needed to make proper and careful tax audits such as hiring experts. In addition, it should be taken into account that the desired outcome of minimizing tax evasion problem by both from rising tax penalty rates and the frequency of tax audits may be outweighed by an increase in tax rates. Policy makers could impose increases in tax rates in order to raise tax revenues. In this case, it is not certain how taxpayers will respond to this increase and it may be possible to become motivated to hide their income when they face an increasing tax rate as we will show in the next sections of the thesis. Clotfelter (1983) also showed that an increase in US tax rates tends to encourage tax evasion.
Mccue, D. & E. S. Belsky 2007. Why Do House Prices Fall?: Perspectives on the Historical Drivers of Large Nominal House Price Declines, Joint Center for Housing Studies, Graduate School of Design [and] John F. Kennedy School of Government, Harvard University.
The interest in reducing the incidence of food-borne diseases, such as salmonellosis caused by pork is increasing. All stages in the porksupply chain can take preventive and reductive measures to decrease the Salmonella prevalence. But it is necessary to have insight in the effect of these measures on the final prevalence of contaminated carcasses. In this way imposing expensive but ineffective measures can be avoided. In order to be able to obtain such evaluations, a stochastic state- transition model is designed. Five stages are included (from piglet to carcass) and two risk-profiles are formulated for each stage: high-risk and low-risk. Scenario studies with the model indicate that all stages may contribute to an increased food safety. The impact of the multiplying stage is limited, because the animals may recover during the finishing stage. Recovery after the finishing stage is not possible, although the transport and lairage can prevent further transmission. At the slaughterhouse the number of contaminated carcasses is highly determined by the prevalence of the supplied animals and the risk profile. Measures in the finishing stage are effective in the reduction of Salmonella in pork, but may be cancelled out if the following stages do not take preventive and reductive measures.
The price difference between farm and retail levels is called price spread, which is constituted mostly by marketing costs and profits. From the price spread, this paper intends to estimate elasticities of price transmission for pork in Malaysia via different empirical model specifications of markup pricing model. Using data from January 1997 to December 2007, a quantitative analysis of farm-to-retail price spreads was undertaken for pork in Malaysia. It was found that retail price is the only variable which is significant. The farm-retail price transmission for pork is very elastic.
There has been a spate of changes in pork industry in Malaysia that precipitated by the doubling of feed, production, and marketing costs over the years. The unprecedented crises in 2008 – namely oil crisis, food crisis, and financial crisis did not only mark the end of cheap food era but also the end of cheap feed era in a more uncertain economic environment. Started off with crude oil crisis, the cost of expensive crude oil passed through and caused an increase in the price carbon-based fertilizers and agro-chemicals used as inputs, through an increase in the cost of operation as well as in transportation and freight. With no option, Malaysia - as a net importer of feed had to continue importing expensive feed. Such unintended burden was even slugged by the food crisis before the tsunami of the financial crisis at latter stage.
Dividend policy, page 12 significant impact on the share pricevolatility. The relationship is not reduced much even after controlling for the above mentioned factors. This suggests that dividend policy affects stock pricevolatility and it provides evidence supporting the arbitrage realization effect, duration effect and information effect in Zimbabwe. The responsiveness of the dividend yield to stock pricevolatility increased during Multiple Currency period (2009- 2011). Whereas payout ratio measure is having significant impact only at lower level of significance. In overall period the size and leverage have positive and significant impact on stock pricevolatility. The size effect is negative during pre Multiple Currency period (2001-2008) but positive during the Multiple Currency period. The earning volatility impact is negative and significant only during Multiple Currency period. Although the results are not robust enough as in the case of developed markets, they are consistent with the behaviour of emerging markets
Du et al. (2010) explored that oil prices effect China’s economic growth and inflation but no effect on China’s output is found on global oil prices, hence oil price exogenous with respect to China. The study considers structural break in the VAR model because of China’s reforms. Jbir and Ghorbel (2009) explored that the oil price shocks have no direct effect on economic activity but these shocks indirectly affected the economic activity via government expending in Tunisia. The variance decomposition explains that the oil price fluctuation is the leading source of government spending changes. Berument and Tasc (2002) estimated the inflationary effect of crude oil prices by using 1990 input-output table for Turkey. The inflationary effect is limited to fixed nominal wages, profits, rent earnings and interest but, when wages, profits, interest and rent earnings are adjusted to the general price level, the inflationary impact of oil prices becomes significant.
The main objective of this paper is to investigate whether oil price uncertainty affects the real effective exchange rate under the floating exchange rate regime. Monthly data of real effective exchange rate and real oil prices from July 1997 to December 2013 are used. The two-stage approach, which comprises a bivariate GARCH model and the standard Granger causality test, is adopted. The main finding is that real oil pricevolatility (uncertainty) does not cause real effective exchange rate of depreciate or appreciate, but real oil pricevolatility does cause real exchange rate volatility (uncertainty) to increase. Real exchange rate uncertainty can impose a significantly negative impact on the country exports and may cause trade deficits. The present paper is structured as follows: Section 2 describes the data used in the analysis and econometric methodology pertaining to a bivariate generalized autoregressive conditional heteroscedastic (GARCH) model and causality test. Section 3 presents empirical results and findings. The last section gives concluding remarks.
For this reason, multi-period volatility is a key ingredient in the long-term price risk measure, see, e.g., Ghy- sels et al. ; Kinateder & Wagner . The most popular method for modelingvolatility is the GARCH type models which capture most of the stylized features of returns volatility in a fairly parsimonious and convincing way. In general, standard GARCH model is hard to be outperformed in terms of forecasting volatility at short horizons (Hansen & Lunde ), whereas when extending the forecast horizon beyond one day, issues related to the proper modeling of long-term volatility dependencies become especially important (Andersen & Bollerslev, ). Put another way, volatility models that account for long memory (LM, or long-range dependence), are able to generate better out-of-sample forecast at long horizons. Actually, long memory is an intrinsic property of time series behavior of financial market volatility and refers to a slow decay of the autocorrelation function of stan- dard proxies of volatility, i.e., the squared returns or absolute returns. Of particular interest is Baillie et al. , they proposed the Fractionally Integrated GARCH (FIGARCH) model to depict the long memory in volatility. In contrast to a GARCH model (an I(0) process, d = 0 ) in which shocks die out at an exponential rate, or an IGARCH model (an I(1) process, d = 1 ) in which there is no mean reversion, shocks to FIGARCH model (an I(d) process) with 0 < < d 1 dissipate at a slow hyperbolic rate. Accounting for long memory yields an addi- tional improvement in specification of volatility models and further impact on the term structure of volatility. Furthermore, numerous recent studies have shown that volatility of commodities with increasing financialization at long horizon can be more accurately forecasted through accounting for long memory via FIGARCH model especially for energy commodities and precious metals (e.g., Elder et al. ; Cunado et al. ; Aloui et al. ; Arouri et al. ; Charles et al., ; Charfeddine  and Youssef et al. ). Thus, long memory is in- dispensable to model and forecast the multi-period volatility and long-term price risk of inventory in SCF.
It is well documented in the literature that house prices are closely associated with macro- economic variables, such as inflation, employment, interest rates, and stock prices. This affect economic cycles (e.g. Higgins and Osler, (1999); Collyns and Senhadji, (2002); Leamer, (2007)) and economic growth, Van Dijk et al. (2009). However, explaining house price movements entails more than mere macroeconomic factors. Household attitudes towards purchases of houses vary along a consumption-investment spectrum. The exact point that households position themselves on this spectrum is critical for the functioning of the housing market: it affects demand, supply and, by implication, the price outcome. For example, if households view house purchases as an act of consumption, they are more likely to hold on to their real asset, rather than, in the event that they wish to make an alternative investment or perceive that they can realize a capital gain, considering entering a secondary market and selling it. In such cases, the supply of houses is doomed to rise at a lower pace than demand, constantly pushing house prices higher. By contrast, if households are willing to consider house purchases as an act of investment, their entrance into a secondary market is more likely, giving supply a greater chance to keep up with rising demand and making house prices more flexible downwards. It, therefore, becomes obvious that meaningful investigation of house price behaviour has to include, but also go well beyond, standard demand and supply analysis.
There exists a widespread belief in the farm sector that price transmission in agri-food supply chains is asymmetric. Increases in farm prices are believed to be matched faster at the retail level whereas negative shocks at the farm level take more time to be passed on to consumers. These concerns have recently been validated to some extent in the literature (see for example Azzam, 1999; Abdulai, 2002; Serra and Goodwin, 2003). Important shocks to agri-food supply chains (e.g., the 2003 Canadian mad cow incident or the 1998 North American hog crisis) usually amplify the perceived problems associated with price transmission. Concentration in downstream markets and input manufacturing industries are often pointed out (perhaps wrongly) to explain why in certain instances, decreases in upstream prices are not accompanied by proportional decreases in downstream prices. There are however other possible explanations for Asymmetric Price Transmission (APT) such as inventory management, menu costs of changing prices, and other adjustment costs (Meyer and von Cramon-Taubadel, 2004).