Analysis of Oil SeedsGrain Price Volatility in India A 印度市糧食價(jià)格波動(dòng)分析及糧食價(jià)格波動(dòng)分析

1、Analysis of Oil Seeds & Grain Price Volatility in India: A VEC-MVGARCH ApproachA Research Proposal byDr Alok Pandey, Ph.D.Associate Professor (Finance)IMT GhaziabadBackgroundOilseeds and wheat grains have witnessed unprecedented volatilities and price fluctuations in the recent past.Extreme volatili

2、ty in commodity prices, particularly of food commodities, affects producers, consumers, traders, exporters & food procurement agencies of the central and state Government. Commodities Under StudyWheat Selected Edible Oil seeds and Oil Wheat & Edible Oil Price Forecast World Wheat Price VolatilityWho

3、 plays the biggest role in pushing the global wheat prices now? It is India. Following Indias plan to buy more wheat for buffer stock, the commoditys prices soared across the world with the World Food Programme (WFP) expressing concern over the impact of dwindling stocks of the cereal. Wheat Price V

4、olatility After India invited tenders for an unspecified quantity of wheat from the international market, the price of wheat crossed record levels on commodity exchanges on Thursday. As grain traders reacted to urgent tenders from grain importers and the lowest global stock levels for 25 years, the

5、prices shot up across the globe. India is the worlds second-largest wheat producer after China, but orders from Delhi to build up buffer stocks pushed price of a bushel climbing 30 cents to $7.88 a bushel on the Chicago Board of Trade. Wheat Price VolatilityIn France, the price of November milling w

6、heat also soared. Natural calamities like droughts and floods and production shortfalls, burgeoning demand and dwindling stocks also created a harvest season panic that again pushed the prices of wheat further.Since April, it has risen 75 per cent on both sides of the Atlantic after recent tenders f

7、rom Egypt and India. Wheat Price VolatilityIndia last year suffered a weak harvest and entered the world market aggressively to import wheat. The International Grains Council expects India to import more than three million tonnes this year, despite an improved harvest. Analysts believe that there is

8、 growing anxiety that the country had benefited from a succession of good monsoons. Wheat Price VolatilityThe International Grain Council cut its forecast of world grain production by seven million tonnes this month to 607 million tonnes, as it assessed the impact of a wet summer in Northern Europe,

9、 weak output in Ukraine and drought in Argentina and Australia.Chicago Board of Trade wheat Futures contract set a new all-time high this week as crop concerns roil the market again. The December contract took out last weeks previous all-time high of $7.54. Wheat Price VolatilityParis wheat Futures

10、settled just shy of their all-time high and London-based wheat Futures surpassed their previous top. More talk of Australian drought conditions and wheat crop woes there was another reason for bulls to buy. Spot Price Volatility (Wheat) Oil & OilseedsOil & Oilseeds Caster Seed / Caster Oil Coconut O

11、il /Copra Cotton Seed /Cottonseed Oil Crude Palm Oil Ground Nut /Groundnut Oil Kapasia Khalli Linseed / Linseed Oil Oil & OilseedsMustard Oil /Mustard Seed /Mustard Seed Oil RBD Palmolein /Refined Soy Oil Refined Sunflower Oil Rice Bran Refined Oil Safflower /Safflower Oil Sesam Oil Soy Meal /Soybea

12、n /Soyabean Oil /Sunflower Oil/Sunflower SeedOil & Oil SeedsIndia is the worlds fourth largest edible oil economy with 15,000 oil mills, 689 solvent extraction units, 251 Vanaspati plants and over 1,000 refineries employing more than one million people. The total market size is at Rs. 600,000 Mln. a

13、nd import export trade is worth Rs.130,000 Mln. Oil & Oil SeedsIndia being deficient in oils has to import 40% of its consumption requirements. With an annual consumption of about 11 mln. Tonnes, the per capita consumption is at 11.50 kgs, which is very low compared to world average of 20 kgs. China

14、 is currently at 17 kg. Overview of Edible Oil EconomyIndian vegetable oil is worlds fourth largest after USA, China and Brazil. Oilseed cultivation is undertaken across the country in two seasons, in about 26 million hectares; mainly on marginal lands, dependent on monsoon rains (un-irrigated) and

15、with low levels of input usage. Yields are rather low at less than one ton per hectare.Three oilseeds - Groundnut, Soybean and Rapeseed/ Mustard - together account for over 80 per cent of aggregate cultivated oilseeds output. Mustard seed alone contributes Rs.120,000 Mln. turnover out of Rs.600,000

16、Mln. oilseed based Sector domestic turnover. Cottonseed, Copra and other oil-bearing material too contribute to domestic vegetable oil poolOverview of Edible Oil EconomyCurrently, India accounts for 7.0% of world oilseeds output; 7.0% of world oil meal production; 6.0% of world oil meal export; 6.0%

17、 of world veg. oil production; 14% of world veg. oil import; and 10 % of the world edible oil consumptionWith steady growth in population and personal income, Indian per capita consumption of edible oil has been growing steadily. However, oilseeds output and in turn, vegetable oil production have be

18、en trailing consumption growth, necessitating imports to meet supply shortfall.Overview of Edible Oil EconomyOverview of Edible Oil Economy(Quantity in Million Tonnes)Crop2-Jan3-Feb4-Mar5-Apr05-06 (F)Major OilseedsGroundnut74.48.266.4Rape/Mustard6.67Soybean5.86.5Other Six32.233.73.

19、6Sub-Total20.715.1 *25.322.123.5OthersCottonseed6.68.5Copra0.70.6Grand Total26.720.331.529.432.6 * Reduced due to Drought.80 per cent of Indias domestic oil output comes from the primary source that is nine cultivated oilseeds and two major oil-bearing materials (Cottonseed and Cop

20、ra). The secondary source comprises of solvent extracted oils, Rice bran oil, oils from minor and tree-borne oilseeds etc. Overview of Edible Oil EconomyMarket PotentialThe per capita consumption of oil in India is 11.5 kg/year is way below the world average of 18 kg. Even china is at 17 kg. By 2021

21、 the per capita consumption of oil in India is likely to be 15.6 kg. There is huge potential of growth. The demand for edible oils is expected to increase from Oil Year 2004-05 levels of 10.9 Mln. tonnes to 12.3 Mln. tonnes by 2006-07 (two years). This assumes a per capita consumption increase of 4%

22、 and a population growth of 1.9% which translates to an overall growth in demand 6% p.a. Based on the above assumptions, edible oil demand in the year 2021 is expected to be 21.3 million tonnes.Demand Projection Edible Oil200420102015Total Demand (Mln. Tonnes)10.915.621.3Total Area under Oilseeds (M

23、ln. Hectares)23.42832Yield (Tonnes/hectare)1.071.21.4Production of Oilseeds (Mln. tonnes)25.133.644.8Domestic supply of edible oils (Mln. tonnes)710.113.4Total edible oil imports - (Mln. tonnes)Imports as share of demand39.40%38.10%39.50%Demand Projection (Contd.)India will continue depende

24、nce on imports to the extent of 40% of its consumption requirements. The improvement in yields and the increase in area under cultivation will ensure that the domestic oilseed production is sufficient to meet 60% of consumption requirements. Increased support from the Government Year Minimum support

25、 Price Rs. per MTFY200111,000FY200212,000FY200313,000FY200416,000FY200517,000FY200617,250Increased support from the GovernmentThe government is increasing its focus on the edible oil industry, given that it has the second largest import bill after crude petroleum. The supported price of mustard seed

26、, which was Rs 11,000 per MT in 2001, was increased to Rs 17,250 per MT by 2006. Consequently, mustard seed cultivation also increased from 5 MMT to 7.0 MMT in 2006. The main emphasis of the government is on reducing the import bill, and this step has helped to a certain extent.Spot Price Volatility

27、 (Wheat) Spot Price Volatility (RM Seed Oil) Spot Price Volatility (Refined Soy Oil)ObjectivesThis paper proposes a multivariate vector error-correction generalized autoregressive conditional heteroscedasticity model to investigate the effect of oilseeds and wheat grain prices in neighbouring countr

28、ies of Asia on its Indian equivalents. We propose to test whether in the long run the law of one price holds and whether in the short run the model captures the salient features of Indian commodity prices (oilseeds and wheat grain). Objectives (Contd.)This model will be used to compute rolling forec

29、asts of the conditional means, variances and covariance of the prices of oilseeds and wheat grain one year ahead. We expect that this model will produce superior forecasts compared to those based on a commonly used methodology of an autoregressive conditional mean model where the second moments are

30、estimated using a fixed weight moving average.ObjectivesTo measure the degree of price instability of important agricultural commodities in the major international and domestic markets. The commodities selected for the study are wheat, palm oil, groundnut oil, soybean oil and coconut oil.To Compare

31、the patterns of variability in Asian markets and understand its implications for Indian producers and consumers. Objectives (Contd.)To examine whether the conditional mean relationship between Asian and Indian grain and oilseed prices can be characterized by a vector error correction (VEC) model.To

32、examine how well do the one-year ahead forecasts of the conditional first and second moments from the VEC-MVGARCH model compare with those generated using the Chavas and Holt (1990) methodology and whether there is a significant difference in these forecasts using Hansens (2001) recently developed t

33、est of superior predictive ability (SPA).MethodologyThe research methodology broadly is based on following three steps:1. Modeling the Mean and Volatility of Indian oilseeds and wheat grain prices using ARCH, GARCH and ARIMA models.2. Testing the data to examine whether the conditional mean relation

34、ship between Asian (few select countries independently) and Indian oilseed and wheat grain prices can be characterized by a vector error correction (VEC) model based on short and long run theory of Law of One Price (LOP).3. Expanding the VEC model to allow for the modeling of the time varying second

35、 moments of domestic oilseeds and grain prices using a MVGARCH model.Standard Approach to Estimating Volatility Define sn as the volatility per day between day n-1 and day n, as estimated at end of day n-1Define Si as the value of market variable at end of day iDefine ui= ln(Si/Si-1)Simplifications

36、Usually Made Define ui as (Si-Si-1)/Si-1Assume that the mean value of ui is zeroReplace m-1 by mThis givesWeighting SchemeInstead of assigning equal weights to the observations we can setARCH(m) Model In an ARCH(m) model we also assign some weight to the long-run variance rate, VL:EWMA Model In an e

37、xponentially weighted moving average model, the weights assigned to the u2 decline exponentially as we move back through timeThis leads toAttractions of EWMARelatively little data needs to be storedWe need only remember the current estimate of the variance rate and the most recent observation on the

38、 market variableTracks volatility changesRiskMetrics uses l = 0.94 for daily volatility forecastingGARCH (1,1) In GARCH (1,1) we assign some weight to the long-run average variance rateSince weights must sum to 1g + a + b =1GARCH (1,1) continuedSetting w = gV the GARCH (1,1) model isandExample Suppo

39、seThe long-run variance rate is 0.0002 so that the long-run volatility per day is 1.4%Example continuedSuppose that the current estimate of the volatility is 1.6% per day and the most recent percentage change in the market variable is 1%.The new variance rate isThe new volatility is 1.53% per dayGAR

40、CH (p,q) Maximum Likelihood MethodsIn maximum likelihood methods we choose parameters that maximize the likelihood of the observations occurringExample 1We observe that a certain event happens one time in ten trials. What is our estimate of the proportion of the time, p, that it happens?The probabil

41、ity of the event happening on one particular trial and not on the others isWe maximize this to obtain a maximum likelihood estimate. Result: p=Example 2Estimate the variance of observations from a normal distribution with mean zeroApplication to GARCHWe choose parameters that maximizeVariance Target

42、ingOne way of implementing GARCH(1,1) that increases stability is by using variance targetingWe set the long-run average volatility equal to the sample varianceOnly two other parameters then have to be estimatedHow Good is the Model?The Ljung-Box statistic tests for autocorrelationWe compare the aut

43、ocorrelation of theui2 with the autocorrelation of the ui2/si2Correlations and Covariances Define xi=(Xi-Xi-1)/Xi-1 and yi=(Yi-Yi-1)/Yi-1Alsosx,n: daily vol of X calculated on day n-1sy,n: daily vol of Y calculated on day n-1covn: covariance calculated on day n-1The correlation is covn/(su,n sv,n)Up

44、dating CorrelationsWe can use similar models to those for volatilitiesUnder EWMAcovn = l covn-1+(1-l)xn-1yn-1Positive Finite Definite Condition A variance-covariance matrix, W, is internally consistent if the positive semi-definite conditionfor all vectors wExampleThe variance covariance matrixis no

45、t internally consistentModelling VolatilityTake a structural modelwith ut N(0,2)typically assumes homoscedasticityif the variance of the errors is not constant this would imply that standard error estimates could be wrong.Is the variance of the errors likely to be constant over time? Not for financi

46、al data.Modelling VolatilitySo can we model time-varying volatility of the errors?Recall the definition of the variance of ut:t2 = Var(ut ut-1, ut-2,.) = E(ut-E(ut)2 ut-1, ut-2,. = Eut2 ut-1, ut-2,.since E(ut) = 0 What might variance of u depend on?Lagged squared errorsThis is Engles ARCH(1) modelAu

47、toRegressive Conditional Heteroscedasticity (ARCH)Easily generalisable to an ARCH(q) formOften large values of q required to capture volatility processesComes with problemsmany coefficients to estimatenon-negativity constraintsvariance cannot be negative so estimated alphas all need to be positive t

48、o ensure definitely positive variance for all errorsGeneralised ARCH (GARCH)Allow conditional variance to also depend on its own lagged value:This is a GARCH(1,1) modelA GARCH(p,q) model follows:GARCH(1,1) ModelGARCH(1,1) ModelGARCH(1,1) is a restricted infinite order ARCH modelyet only needs three

49、parameters to be estimated0 is the constant1 is the effect of last periods error1is the effect of last periods variance1 + 1 gives the persistence of the volatility:1 + 1 1 implies volatility explodesMore about GARCHConditional variance is time-varying and can be modelled by GARCHUnconditional varia

50、nce is constant, and is given byThis is defined 1+1 0Non-negativity constraint is 00, 10, 10 and 1+0News Impact CurvesNICs plot this impact of a shock (“news) on conditional varianceExtensionsGARCH-in-meanFinance suggests that expected returns depend on expected riskTodays returns should depend on todays (sometimes yesterdays) c