Inflation v/s commodity market
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Transcript of Inflation v/s commodity market
8/2/2019 Inflation v/s commodity market
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INFLATION & COMMODITY MARKET
PROJECT DONE BY,
BHAVIKA THAKKER
PGDBM
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OBJECTIVE OF THE PROJECT
Testing the market efficiency
If the market are efficient than predicting spotprices through futures
Establishing relation between inflation & economic indicators like money supply, volume incommodity market, crude oil & historical inflation
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Testing The Market Efficiency
VOLUME IS INCREASING
RETURNS ARE INCREASING
INFLATION IS RISING
GOVERNMENT IS INTERVENING
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Volume analysis
volume ananlysis
0.00
100000.00
200000.00
300000.00400000.00
500000.00
D e c 0 3
M a y
O c t 0 4
M a r 0 5
A u
g 0 5
J a n 0 6
J u
n 0 6
N o v 0
6
A p r 0 7
S e p 0 7
F e b 0 8
Month
v o l u m e
MCX
NCDEX
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Comparing the risk and returns of different asset classes
MEAN (Annual) CAGR STD DEV (Annual)
G-SEC 10 YR BOND 7.51%
MCX GOLD 21.90% 21.67% 15.94%
MCX COPPER 30.74% 29.04% 30.94%
MCX CRUDE 31.34% 30.81% 26.85%
NIFTY 31.18% 31.60% 23.47%
SENSEX 29.62% 29.36% 24.48%
SILVER 30.74% 29.04% 30.94%
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0.005000.00
10000.0015000.0020000.0025000.0030000.0035000.0040000.0045000.00
1 / 3 1 / 2 0 0 5
5 / 3 1 / 2 0 0 5
9 / 3 0 / 2 0 0 5
1 / 3 1 / 2 0 0 6
5 / 3 1 / 2 0 0 6
9 / 3 0 / 2 0 0 6
1 / 3 1 / 2 0 0 7
5 / 3 1 / 2 0 0 7
9 / 3 0 / 2 0 0 7
1 / 3 1 / 2 0 0 8
5 / 3 1 / 2 0 0 8
coppersilver
gold
crude
sensexnifty
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INCREASE IN WPIWPI
050
100150200250
4 / 3 0 / 1 9 9 4
2 / 2 8 / 1 9 9 5
1 2 / 3 1 / 1 9 9 5
1 0 / 3 1 / 1 9 9 6
8 / 3 1 / 1 9 9 7
6 / 3 0 / 1 9 9 8
4 / 3 0 / 1 9 9 9
2 / 2 9 / 2 0 0 0
1 2 / 3 1 / 2 0 0 0
1 0 / 3 1 / 2 0 0 1
8 / 3 1 / 2 0 0 2
6 / 3 0 / 2 0 0 3
4 / 3 0 / 2 0 0 4
2 / 2 8 / 2 0 0 5
1 2 / 3 1 / 2 0 0 5
1 0 / 3 1 / 2 0 0 6
8 / 3 1 / 2 0 0 7
TIME
W P I
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METHOD FOLLWED FOR TESTINGMARKET EFFICIENCY
COINTEGRATION
Definition : If there exist a stationarylinear combination of non stationaryrandom variables, the variables combinedare said to be cointegrated.
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DATA COLLECTIONOne three month contract of refined soya oil is taken (Apr, 08) fromNCDEX database. Three time series of spot and future is been constructedfor the analysis.
Series 1: First series is made with the seven days lag of future prices byassuming market players know what would be the demand supplyscenario after the seven days and can predict spot prices in precisemanner.
Series 2: Second series is constructed with the fourteen days lag of future prices to check whether market players know what would be thedemand supply scenario after the fourteen days and are they able to
predict spot prices or no?Series 3 & 4: Similarly third and fourth time series are constructed withthe one and two month lag of future prices respectively to check the longterm and short term equilibrium between spot and future prices.
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ProcedureDetermine whether yt and xt are I(1). This is equivalent to
determining whether or not they have unit roots
Provided they are both I(1), estimate the parameters of thecointegrating relation
Test to find least squares residual appears to be I(0)
If the series is cointegrated we put restriction on vector to seethe future prices are unbiased indicator of spot . We check thisbecause high speculation leads to the increase in future priceswhich may not be the pure or unbiased indicator of spot
After checking for restriction if we get the cointegration
relationship we fit the model into VECM to get the cointegratingcoefficients.
Coefficients can be used for forecasting the spot prices in thattime horizon.
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FINDINGS..For all the three series we got cointegration and aftergetting the cointegration linear trends were established andcointegrating vectors were measured.
Only for first series equilibrium was obtained even afterputting the restriction on b coefficients. This can beinterpreted as that the future prices are unbiased predictorof spot in seven days horizon i.e. future market player hasthe idea of what would be the spot price in seven days.
When market is in this equilibrium we can predict the spotprice seven days in advance
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Apr 08 contract: prediction sevendays in advance
-100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71
spot pricespredicted
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Mar 08 Contract: Prediction SevenDays Prior:
-
100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
Series1
Series2
(Series 1 represents actual spot prices and series 2 predicted values)
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INFLATION
Inflation is a rise in the general level of prices over time. It may also refer to a
rise in the prices of a specific set of goodsor services. In either case, it is measuredas the percentage rate of change of aprice index
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Causes of inflation
Demand-pull inflation
Cost-push inflation
Built-in inflation
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Causes of current inflation rise:
Wage pressure
Commodity prices and expectations
Imported Inflation
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GROWTH IN INFLATION INDEX
-2
0
2
4
6
8
10
12
14
1 / 3 1 / 2 0 0 0
6 / 3 0 / 2 0 0 0
1 1 / 3 0 / 2 0 0
4 / 3 0 / 2 0 0 1
9 / 3 0 / 2 0 0 1
2 / 2 8 / 2 0 0 2
7 / 3 1 / 2 0 0 2
1 2 / 3 1 / 2 0 0
5 / 3 1 / 2 0 0 3
1 0 / 3 1 / 2 0 0
3 / 3 1 / 2 0 0 4
8 / 3 1 / 2 0 0 4
1 / 3 1 / 2 0 0 5
6 / 3 0 / 2 0 0 5
1 1 / 3 0 / 2 0 0
4 / 3 0 / 2 0 0 6
9 / 3 0 / 2 0 0 6
2 / 2 8 / 2 0 0 7
7 / 3 1 / 2 0 0 7
1 2 / 3 1 / 2 0 0
5 / 3 1 / 2 0 0 8
US CPI
CHINA CPI
FRANCE CPI
JAPAN CPI
INDIA WPI
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EFFECT OF INFLATION ON GOLD
0
2000
4000
6000
8000
10000
12000
14000
1 1 / 3 0 / 2 0 0 3
3 / 3 1 / 2 0 0 4
7 / 3 1 / 2 0 0 4
1 1 / 3 0 / 2 0 0 4
3 / 3 1 / 2 0 0 5
7 / 3 1 / 2 0 0 5
1 1 / 3 0 / 2 0 0 5
3 / 3 1 / 2 0 0 6
7 / 3 1 / 2 0 0 6
1 1 / 3 0 / 2 0 0 6
3 / 3 1 / 2 0 0 7
7 / 3 1 / 2 0 0 7
1 1 / 3 0 / 2 0 0 7
3 / 3 1 / 2 0 0 8
wpi
gold
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EFFECT OF INFLATION ON NSE
-200
-150
-100
-50
0
50
100
150
200
9 / 3
0 / 1 9 9 8
7 / 3
1 / 1 9 9 9
5 / 3
1 / 2 0 0 0
3 / 3
1 / 2 0 0 1
1 / 3
1 / 2 0 0 2
1 1 / 3
0 / 2 0 0 2
9 / 3
0 / 2 0 0 3
7 / 3
1 / 2 0 0 4
5 / 3
1 / 2 0 0 5
3 / 3
1 / 2 0 0 6
1 / 3
1 / 2 0 0 7
1 1 / 3
0 / 2 0 0 7
0
1
2
3
4
5
67
8
9
10
nse
inflation
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CORELATION MATRIX
WPI(-1) VOLUME(-1) M3(-1) WTI(-1)
WPI(-1) 1 0.93094256 0.98165113 0.90283515
VOLUME(-1) 0.93094256 1 0.90283515 0.871265068
M3(-1)
0.98165113
0.909206176
1
0.904038656
WTI(-1) 0.90283515 0.871265068 0.904038656 1
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Establishing relation forinflation
Regression testing for the hypothesis that mispricing of commodity futures contracts leads to inflation is performedby multiple regression
WPI is regressed on the volumes of trade in leading twofutures exchanges in India (NCDEX and MCX), moneysupply (M3) and oil prices.
We took WPI as dependent variable. The independentvariables are one lagged WPI, WTI, money supply (M3)and volume. One lag is taken because WPI figures that weget is it self a laggard variable.
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Multiple regression results
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INFERENCES
Futures price contains better information about the supplyand demand of the commodity when it gets closer to itsmaturity or at least in seven days horizonFor seven days horizon using the cointegration equationcoefficients spot price were forecasted which showed thesame trend as of actual valuesThe regression model is significant as suggested by F test.It suggests that the inflation is persistent as it depends onits past values (approx. 76.32%).Rising crude prices showed next higher effect on theinflation.The growth in volume of trade and growth in money supplyare not so important determinants of inflation as theircoefficients are very small.
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THANK YOU