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Analysis of Oil Seeds

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Analysis of Oil Seeds & Grain Price Volatility in India: A VEC-MVGARCH Approach ... Oilseeds and wheat grains have witnessed unprecedented volatilities and price ... – PowerPoint PPT presentation

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Title: Analysis of Oil Seeds


1
Analysis of Oil Seeds Grain Price Volatility in
India A VEC-MVGARCH Approach
  • A Research Proposal by
  • Dr Alok Pandey, Ph.D.
  • Associate Professor (Finance)
  • IMT Ghaziabad

2
Background
  • Oilseeds and wheat grains have witnessed
    unprecedented volatilities and price fluctuations
    in the recent past.
  • Extreme volatility in commodity prices,
    particularly of food commodities, affects
    producers, consumers, traders, exporters food
    procurement agencies of the central and state
    Government.

3
Commodities Under Study
  • Wheat
  • Selected Edible Oil seeds and Oil
  • Wheat Edible Oil Price Forecast World Bank.xls

4
Wheat Price Volatility
  • Who 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.

5
Wheat Price Volatility
  • 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 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.

6
Wheat Price Volatility
  • In France, the price of November milling wheat
    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 from
    Egypt and India.

7
Wheat Price Volatility
  • India 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 growing anxiety
    that the country had benefited from a succession
    of good monsoons.

8
Wheat Price Volatility
  • The 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,
    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.

9
Wheat Price Volatility
  • Paris wheat Futures 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.

10
Spot Price Volatility (Wheat)
11
Oil Oilseeds
  • Oil Oilseeds
  • Caster Seed /  Caster Oil  
  • Coconut Oil / Copra  
  • Cotton Seed / Cottonseed Oil  
  • Crude Palm Oil
  •  Ground Nut / Groundnut Oil  
  • Kapasia Khalli
  •  Linseed /  Linseed Oil
  •  

12
Oil Oilseeds
  • Mustard 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  /Soybean / Soyabean Oil / Sunflower
    Oil/ Sunflower Seed

13
Oil Oil Seeds
  • India 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. and
    import export trade is worth Rs.130,000 Mln.

14
Oil Oil Seeds
  • India 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 is currently at 17 kg.

15
Overview of Edible Oil Economy
  • Indian vegetable oil is world's 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 with low levels
    of input usage.
  • Yields are rather low at less than one ton per
    hectare.

16
Overview of Edible Oil Economy
  • 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 Mln. oilseed based
    Sector domestic turnover.
  • Cottonseed, Copra and other oil-bearing material
    too contribute to domestic vegetable oil pool

17
Overview of Edible Oil Economy
  • Currently, 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
    of world veg. oil production 14 of world veg.
    oil import and 10 of the world edible oil
    consumption
  • With 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 been trailing consumption
    growth, necessitating imports to meet supply
    shortfall.

18
Overview of Edible Oil Economy(Quantity in
Million Tonnes)
Crop 2-Jan 3-Feb 4-Mar 5-Apr 05-06 (F)
Major Oilseeds
Groundnut 7 4.4 8.2 6 6.4
Rape/Mustard 5.1 3.9 6.2 6.6 7
Soybean 5.6 4.6 7.9 5.8 6.5
Other Six 3 2.2 3 3.7 3.6
Sub-Total 20.7 15.1 25.3 22.1 23.5
Others
Cottonseed 5.1 4.5 5.5 6.6 8.5
Copra 0.9 0.7 0.7 0.7 0.6
Grand Total 26.7 20.3 31.5 29.4 32.6
Reduced due to Drought.
19
Overview of Edible Oil Economy
  • 80 per cent of India's domestic oil output comes
    from the primary source that is nine cultivated
    oilseeds and two major oil-bearing materials
    (Cottonseed and Copra). The secondary source
    comprises of solvent extracted oils, Rice bran
    oil, oils from minor and tree-borne oilseeds etc.

20
Market Potential
  • The 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 2010 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 and a population growth of 1.9
    which translates to an overall growth in demand _at_
    6 p.a. Based on the above assumptions, edible
    oil demand in the year 2015 is expected to be
    21.3 million tonnes.

21
Demand Projection Edible Oil
2004 2010 2015
Total Demand (Mln. Tonnes) 10.9 15.6 21.3
Total Area under Oilseeds (Mln. Hectares) 23.4 28 32
Yield (Tonnes/hectare) 1.07 1.2 1.4
Production of Oilseeds (Mln. tonnes) 25.1 33.6 44.8
Domestic supply of edible oils (Mln. tonnes) 7 10.1 13.4
Total edible oil imports - (Mln. tonnes) 4.3 5.9 8.3
Imports as share of demand 39.40 38.10 39.50
22
Demand Projection (Contd.)
  • India will continue dependence 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.

23
Increased support from the Government
Year Minimum support Price Rs. per MT
FY2001 11,000
FY2002 12,000
FY2003 13,000
FY2004 16,000
FY2005 17,000
FY2006 17,250
24
Increased support from the Government
  • The 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, 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.

25
Spot Price Volatility (Wheat)
26
Spot Price Volatility (RM Seed Oil)
27
Spot Price Volatility (Refined Soy Oil)
28
Objectives
  • This paper proposes a multivariate vector
    error-correction generalized autoregressive
    conditional heteroscedasticity model to
    investigate the effect of oilseeds and wheat
    grain prices in neighbouring countries 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).

29
Objectives (Contd.)
  • This model will be used to compute rolling
    forecasts 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 estimated
    using a fixed weight moving average.

30
Objectives
  • To 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 the patterns of variability in Asian
    markets and understand its implications for
    Indian producers and consumers.

31
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 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 test of superior predictive ability
    (SPA).

32
Methodology
  • The 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 relationship 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 moments of
    domestic oilseeds and grain prices using a
    MVGARCH model.

33
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-1
  • Define Si as the value of market variable at end
    of day i
  • Define ui ln(Si/Si-1)

34
Simplifications Usually Made
  • Define ui as (Si-Si-1)/Si-1
  • Assume that the mean value of ui is zero
  • Replace m-1 by m
  • This gives

35
Weighting Scheme
  • Instead of assigning equal weights to the
    observations we can set

36
ARCH(m) Model
  • In an ARCH(m) model we also assign some weight
    to the long-run variance rate, VL

37
EWMA Model
  • In an exponentially weighted moving average
    model, the weights assigned to the u2 decline
    exponentially as we move back through time
  • This leads to

38
Attractions of EWMA
  • Relatively little data needs to be stored
  • We need only remember the current estimate of the
    variance rate and the most recent observation on
    the market variable
  • Tracks volatility changes
  • RiskMetrics uses l 0.94 for daily volatility
    forecasting

39
GARCH (1,1)
  • In GARCH (1,1) we assign some weight to the
    long-run average variance rate
  • Since weights must sum to 1
  • g a b 1

40
GARCH (1,1) continued
  • Setting w gV the GARCH (1,1) model is
  • and

41
Example
  • Suppose
  • The long-run variance rate is 0.0002 so that the
    long-run volatility per day is 1.4

42
Example continued
  • Suppose 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 is
  • The new volatility is 1.53 per day

43
GARCH (p,q)

44
Maximum Likelihood Methods
  • In maximum likelihood methods we choose
    parameters that maximize the likelihood of the
    observations occurring

45
Example 1
  • We 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 probability of the event happening on one
    particular trial and not on the others is
  • We maximize this to obtain a maximum likelihood
    estimate. Result p0.1

46
Example 2
  • Estimate the variance of observations from a
    normal distribution with mean zero

47
Application to GARCH
  • We choose parameters that maximize

48
Variance Targeting
  • One way of implementing GARCH(1,1) that increases
    stability is by using variance targeting
  • We set the long-run average volatility equal to
    the sample variance
  • Only two other parameters then have to be
    estimated

49
How Good is the Model?
  • The Ljung-Box statistic tests for autocorrelation
  • We compare the autocorrelation of the
  • ui2 with the autocorrelation of the ui2/si2

50
Correlations and Covariances
  • Define xi(Xi-Xi-1)/Xi-1 and yi(Yi-Yi-1)/Yi-1
  • Also
  • sx,n daily vol of X calculated on day n-1
  • sy,n daily vol of Y calculated on day n-1
  • covn covariance calculated on day n-1
  • The correlation is covn/(su,n sv,n)

51
Updating Correlations
  • We can use similar models to those for
    volatilities
  • Under EWMA
  • covn l covn-1(1-l)xn-1yn-1

52
Positive Finite Definite Condition
  • A variance-covariance matrix, W, is internally
    consistent if the positive semi-definite
    condition
  • for all vectors w

53
Example
  • The variance covariance matrix
  • is not internally consistent

54
Modelling Volatility
  • Take a structural model
  • with ut ? N(0,s2)
  • typically assumes homoscedasticity
  • if 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 financial data.

55
Modelling Volatility
  • So can we model time-varying volatility of the
    errors?
  • Recall the definition of the variance of ut
  • st2 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 errors
  • This is Engles ARCH(1) model

56
AutoRegressive Conditional Heteroscedasticity
(ARCH)
  • Easily generalisable to an ARCH(q) form
  • Often large values of q required to capture
    volatility processes
  • Comes with problems
  • many coefficients to estimate
  • non-negativity constraints
  • variance cannot be negative so estimated alphas
    all need to be positive to ensure definitely
    positive variance for all errors

57
Generalised ARCH (GARCH)
  • Allow conditional variance to also depend on its
    own lagged value
  • This is a GARCH(1,1) model
  • A GARCH(p,q) model follows

58
GARCH(1,1) Model
59
GARCH(1,1) Model
  • GARCH(1,1) is a restricted infinite order ARCH
    model
  • yet only needs three parameters to be estimated
  • a0 is the constant
  • a1 is the effect of last periods error
  • ß1is the effect of last periods variance
  • a1 ß1 gives the persistence of the volatility
  • a1 ß1 lt 1 implies volatility decays
  • a1 ß1 ? 1 implies very slow decay
  • a1 ß1 gt 1 implies volatility explodes

60
More about GARCH
  • Conditional variance is time-varying and can be
    modelled by GARCH
  • Unconditional variance is constant, and is given
    by
  • This is defined a1ß1 lt 1
  • But not if a1ß1 ? 1, in which case the process
    is non-stationary in variance

61
Estimation of GARCH Models
  • The GARCH-class of models are not like simple
    linear ones we have encountered until now
  • Hence OLS cannot be used
  • essentially, OLS minimises RSS which only depends
    on parameters in the conditional mean equation
  • we want to optimise parameters in the conditional
    variance term so OLS is not useful
  • Instead, maximum likelihood techniques are used

62
Maximum Likelihood
  • The parameters of the model are chosen which are
    most likely to have produced the observed data
  • First, specify the likelihood function
  • an equation that states how likely it is that the
    observed data came from the data generating
    process
  • Then search for the maximum of this (very
    complex) function
  • local versus global maxima

63
Extensions
  • Asymmetric GARCH
  • In a basic GARCH model, the conditional variance
    is determined by last periods variance and last
    periods error squared
  • So a positive error has the same effect on
    variance as a negative error
  • This need not always be a good assumption

64
Leverage Effects
  • Suppose there is a negative shock to the equity
    return of a company
  • This increases the leverage of the firm (equity
    value down, debt unchanged)
  • So the risk of the equity has risen
  • A positive shock to the equity reduces leverage
    and has a negative impact on risk (other things
    ignored)
  • A negative error has a larger effect than a
    positive error

65
GJR Model
  • Glosten, Jagannathan and Runkle proposed
  • Leverage effect would suggest ? gt 0
  • Non-negativity constraint is a0gt0, a1gt0, ß1gt0 and
    a1?gt0

66
News Impact Curves
  • NICs plot this impact of a shock (news) on
    conditional variance

67
Extensions
  • GARCH-in-mean
  • Finance suggests that expected returns depend on
    expected risk
  • Todays returns should depend on todays
    (sometimes yesterdays) conditional standard
    deviation (or sometimes variance)

68
GARCH-in-Mean
  • An increase in risk, given by the conditional
    standard deviation leads to a rise in the mean
    return
  • The value of d gives the increase in returns
    needed to compensate for a give increase in risk
  • So is a measure of risk aversion

69
Extensions
  • Multivariate GARCH
  • Univariate GARCH models capture the evolution of
    conditional variances
  • Multivariate GARCH models also capture movements
    in conditional covariances
  • These look quite complicated and use a lot of
    matrix algebra
  • But are really quite simple (honest)

70
Multivariate GARCH
  • VECH model, 2 asset case
  • we here model the conditional variance-covariance
    matrix
  • 21 parameters to estimate

71
Multivariate GARCH
  • Diagonal VECH model
  • Restricted version of VECH model
  • only 9 parameters to estimate
  • and works pretty well

72
Application
  • Bollerslev, Engleand Wooldridge (1988)
  • Multivariate diagonal VECH GARCH-in-mean model
  • US T-bills (asset 1)
  • US T-bonds (asset 2)
  • US equities (asset 3)
  • 1959Q1-1984Q2

73
Application
74
Interpretation
  • Coefficient of risk aversion was 0.5, in line
    with theory
  • Persistence of shocks to conditional variance
    high for T-bills (0.4450.466) but low for bonds
    (0.1880.441) and stocks (0.0780.469)
  • But stock variances not well captured (no element
    statistically significant)
  • unconditional covariance between bills and bonds
    positive(h12). Negative between bills and stocks
    (h13) and bonds and stocks (h23)
  • since lagged conditional covariances negative and
    larger than error cross-products

75
Practical Uses
  • Time-varying optimal hedge ratio Ht
  • Conditional CAPM betas

76
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77
VEC Models
78
The Chavas Holt Methodology (1990)
79
Hansens Test of SPA (2001)
80
The MV GARCH Model
81
Sources of Data
82
Limitations of the Study
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