Short-term Forecast Of Agricultural Product Market Under The Framework Of SV Factor Analysis

Agricultural product market price is relevant to the livelihood of the people and social stability. Inrecent years, the Central No.1Document and Government Report had several times mentioned tostrengthen agricultural product market Monitoring and early warning so as to ensure that the agriculturalproduct market price does not fluctuate wildly. The price of agricultural product short-term market isinfluenced by multiple complicated factors, which makes short-term fluctuation drastic, short-termmarket risk highlighted, and the difficulty of price forecast increased. Under the situation in whichmarket main body is not mature, market system is not perfect, and legal system environment still is notperfected, etc., agricultural producers often face and take the market risk brought by price fluctuationdue to the difficulty of precise prediction for market supply-demand and price fluctuation. Departmentsof agricultural administration are often short of effective Anticipation information of market priceshort-term trend, and difficult to adopt forward-looking and beforehand control measures. Due to theshortage of timely authority information guide, the consumers are susceptible to panic psychologyduring frequent market prices fluctuation, which hence speeds up the vicious circle of price fluctuation.Therefore, it is of important theoretical and practical significance for us to carry out short-term forecastof agricultural product market, which will help to promote stable agricultural production, increasefarmers' income, and guarantee the supply of agricultural product market.This dissertation has selected pork, chicken, egg, vegetable and fruit as the research object, takenmodern western economics, econometrics, statistics, price theory and other relevant theories as theguide, and adopted12kinds of study methods of non-parameter kernel density estimation, H-P filteringmethod, value at risk based on ARCH Model, event study, quantile regression, multilayer feed-forwardneural network, grey model method, vector auto-regression method, non-stationary time series method(SARIMA Model), season adjustment method (Cencus X12method), Holt-Winters method, andcombination forecasting method. This study has further analyzed the strong fluctuation class factor (Sfactor) and volatile class factor (V factor) that influence agricultural product market price, studied thefluctuation laws of agricultural product market price intertwined by SV factors, focused onbreakthroughs of short-term forecast technology of agricultural product market, constructed short-termprediction models of agricultural product market price based on different information, differenttechnology, and different objectives, and preliminary designed and realized the intelligent forecastingsystem of agricultural product market price. This study has mainly unfolded innovative studies in thefollowing aspects:(1) To counter the complicated and diversified factors influencing agricultural product market price,this study had put forward the thoughts of framework analysis based on SV factors, and implementedstudies on basic theory of SV factors. This study has broken down the factors influencing agriculturalproduct market price into strong fluctuation class factors and volatile class factors, which has provided anew viewing angle for early warning of abnormal market price fluctuation. In addition, it also exploredthe5development phases of forecasting methods, the relations between agricultural product market forecast and relevant disciplined, as well as fluctuation theories of agricultural product market price andother contents.(2) We have done some research on price fluctuation of agricultural product market interwoven bySV factors. Based on the conventional economic factors and non-economic factors from the perspectiveof China, we have reached the conclusion taking vegetable as the empirical study: it shows productioncost (chemical fertilizer), excess liquidity, hot money and climate are the reasons for vegetable marketprice fluctuation by Granger test. The impacts of chemical fertilizer has certain lag phase and thelong-term co-integration vector is [1,-1.18]. The short-term impact of excess liquidity (amount ofcurrency issue) on vegetable price is fairly significant. When the amount of currency issue increases1%,vegetable price rises by1.29%. Second is the impact of climate (temperature) on vegetable priceshort-term fluctuation. When temperature declines or rises by1%, the price of vegetable will rise ordecline0.08%. The short-term impact of hot money on vegetable price is the minimum. Based on thetransmission relationship from the perspective of agricultural product prices of international market,taking corn and soybeans as an example the empirical study result shows: international market pricechange is the reason for domestic market price change, not vice versa by Granger test. The impact ofinternational corn price on domestic corn price is less than that of international soybean price ondomestic soybean price. Viewing the long-term fluctuation relation, the co-integrated vector of domesticcorn price and international corn price is [1,-0.61] and the co-integrated vector of domestic soybeanprice and international soybean price is [1,-0.86]. Viewing the short-term fluctuation relation, the cornprice of prophase rose by1%while domestic corn price of this phase rises0.07%. However, the impactof international soybean market price on domestic soybean market price is synchronous. Wheninternational soybean price of this phase rises1%, the domestic soybean price will rise0.44%.(3) We have adopted non-parameter kernel density technology to fit the distribution of probabilitydensity function (PDF) of11vegetables and12fruits market returns, and constructed the dynamicassessment model of agricultural product market short-term risk base on ARCH model. The results ofkernel density estimation show: the normal distribution, Beta distribution, Burr distribution, Gammadistribution and other empirical distribution are not the best. The probability density distribution ofvegetable and fruit market returns is asymmetric. The risk of price rising is greater than that of pricedeclining, and the phenomenon of prices wildly fluctuation of vegetable and fruit becomes norm.Taking soybean and vegetable as an example, we have used GARCH model to calculate the daily VaR,weekly VaR and monthly VaR of the market returns. The results of empirical approach show: VaRbased on GARCH model can fairly well reflect the distribution and fluctuation of market returns. It cannot only describe the dynamic course of market returns changes, getting adapted to the needs of VaR,but also to a large extent improve the precision of VaR.(4) We have unfolded short term model construction research on agricultural product market basedon S factors. To counter price forecast guided by unexpected events, on the basis of Comparison ofclassic events study method, event study based on dummy variable and event study based on knowledge,we have innovatively put forward IPAD forecasting framework of unexpected events and divided events into4essential elements of intensity, persistence, attenuation model, and impact direction. Tocounter nonlinear change and uncertainty of market price, we have constructed multilayer feed-forwardneural network model. The results show that the precision of pork, chicken and egg forecast is over95%,and the precision of vegetables and fruits forecast is over90%. To counter the problem of small datasample, we have constructed grey forecasting model GM (1,1). The results show that the forecastprecision of pork, chicken meat and egg basically all over95%.(5) We have unfolded short-term model construction research on agricultural product market basedon V factors. In the aspect of structural model, we have constructed the mean regression forecast modeland the quantile regression forecast model respectively, and conducted empirical analysis of pork,chicken and egg. The forecasting results show that the precision for pork price forecast is about90%and the precision for chicken and egg price forecasts is over98%based on mean regression model, andthe precision of quantile regression forecast model is over99%. In the aspect of time series model, wehave constructed the vector error correction model (VEC), SARIMA model, Holt-Winters seasonexponential smoothing model and Census X12season decomposition method. We have undertakenempirical forecasts of five kinds of agricultural products including pork, chicken meat, egg, vegetableand fruit. The results show that the precision of4kinds of time series models are all over90%, of which,the precision of egg and chicken model is over95%.(6) We have unfolded combination forecasts based on forecasting course and forecasting resultsrespectively. Different models have different extraction degree of information. As time goes on theprecision of individual forecast will be not robust, under the situation of increased uncertainty in thefuture, combination forecast often has more advantages. The results of combination forecasts takingvegetable and fruit as an example show that combination forecasts can improve prediction accuracy inmost cases. However, there are also some special exceptions, i.e. combination forecasting result is notas good as single method. When we undertake combination forecasts based on single method that hasgreater forecasting error, the improvement of prediction precision will be greater. If the precision ofindividual methods is very high, the improvement of precision for combined forecasts will be verylimited.