CHAPTER Based on the recent developments in these

CHAPTER
4
CONCEPTUAL/THEORITICAL
FRAMEWORK
4.1
Relationship between Spot and Future Prices .
There
are certain theoretical frameworks which are available through
various literature’s which describes the relationship between spot
and future prices.

The
Samuelson’s (1965) preposition, the net hedging hypothesis etc
which explains the relationship between spot and future prices. Based
on the recent
developments in these directions can be observed in Garbade&
Silber (1983), Foster (1996) and Figuerolla – Ferretti &
Gonzalo (2008) in which the authors tried to provide the context on
the impact of other activities like arbitrage on price behavior.
4.1.1
The Relationship between Spot and Future Prices based on the ‘Basis’

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

The
relationship between spot price and futures price can be easily
understood by studying the ‘basis’ as the behavior. Basis is the
difference between the cash price and the futures price (or) it
defines the relationship between the cash/spot and the futures
contract. Positive basis is called backwardation and negative basis
is called contango. Spot prices are more volatile when the market is
in backwardation
Where,
Basis = Spot Price – Future Price

So
,If we rearrange the equation and solve for Spot price , it will be
Spot price = Basis + Future Price
4.1.2
The Relationship between Spot and Future Prices based on the ‘cost
of carry model’
The
relationship of futures price and spot price is the cost of carry
model. The cost of carry model states that under a perfect market
situation the returns in the spot and the futures market signal of
tight supply and demand condition should be perfectly correlated.
Which the equation below states
Ft
= St e (r-y)*(T-t)/365

4.1.3
The Relationship between Spot and Future Prices based on the ‘Theory
of Storage’

Theory
of storage is an fundamental structure which is used in commodity
price modeling. The convenience yield, as introduced in Kaldor’s
(1939) theory of storage is meant to represent the benefit of holding
the physical commodity instead of a paper contract on that commodity,
hence avoiding the cost of disruption in the production.

Convenience
yield is the implied yield or non- pecuniary return from holding a
commodity. It is a measure of the degree of backwardation in market.
Convenience yield is defined as the difference between the positive
gains attached to the physical commodity less the cost of storage.
Keeping inventory generates lot of advantages –the marginal
convenience yield varies inversely with the level of inventories.

The
relationship between spot and future prices can be written as

ft
= st – (r-y)

4.1.4
The Relationship between Spot and Future Prices, the ‘Samuelson
Effect’

Net
hedging hypothesis tells that the spot price and the future price
will converge at the end of the future contract or at expiration
(Future price = Spot price). The arbitrages make sure that
convergence happen at the expiration. Arbitrage is the simultaneous
purchase (sale) of commodity and sale (purchase) of the corresponding
futures contract, in order to profit from price distortions.

The
futures price and spot price converges at expiration (on the expiry
of the contract). Samuelson (1965) proposition states that the
futures price varies less in comparison to spot prices and the
variation of futures price reduces as maturity approaches. This
effect is called the Samuelson effect.

4.1.5
The Relationship between Spot and Future Prices, Recent Developments

Garbade
& Silber (1983) establish an equilibrium price relationship
between the futures and cash market prices as Fk = Ck + r.?k
(5) Where, Fk is the future price and Ck spot price of a
commodity. r is the continuously compounded yield per unit time,
assumed not to vary with maturity. The authors observe that the cash
and futures markets will be in partial equilibrium if the futures
price will equal the cash price plus a premium which reflects the
deferred payment on a futures contract

CHAPTER
5
DATA
ANALYSIS AND INTERPRETATION
Data
Collection
The
data collected
here are the price series of futures and spot closing prices of 8
agricultural commodities i.e Chana ,Turmeric , Barley,Wheat, Pepper,
Soya Bean, Coriander (Dhanya) and Castor Seed at a daily frequency
which
are available quarterly period
from January 2013 to December 2017. The price series data are
collected from the website like Multi-Commodity Exchange, National
Commodity and Derivatives Exchange Limited (NCDEX) and other
agricultural commodity exchange in India were the percentage of
market share for these agricultural commodity is about 70 percent
based on Average Daily Traded Value (ADTV) and more than 85 percent
based on open interest.

The
rationale selection for these commodities can be because these
commodities represent as the highly traded contracts in the commodity
market so as for the selection goes the commodity are based on the
following for which Wheat is the most essential food grain in India.
. Barley comes in the category of cereals, and is used for making
health food, beer and soups. We also use barley as a cattle feed.
Soyabean is the most widely grown oil seeds in the world and India is
the fifth largest soyabean producer worldwide. Coriander comes in
the category of spices. It has medical use also. India is one of the
major exporter of coriander. In brief, these agricultural crops play
an essential role in the Indian economy.

Further,
these are the commodities for which futures and spot price data are
available from the national exchanges.

We
have to analyze the inter linkages among Chana maize, Turmeric ,
Barley , Wheat , Pepper , Soyabean ,Coriander (Dhanya)and Castor
seed.

In
order to construct the futures price series, the nearby futures
contracts are being used because these are highly liquid and the most
active/regular contracts. So we observe these above variables for the
future contracts based on the delivery month to the day of trading,
which will be specified be
Then
based on consistent and objective criteria, trading activity is taken
as the parameter for splicing the price series.

Whenever
a contract approaches maturity, the market changes its attention away
from the nearby month contract to the next nearby contract before the
nearby contract approaches its last trading day. It is important to
notice that trading volume and open interest for a particular
contract peak three or four weeks prior to the last trading day and
start declining. The present method evades using observations near
the maturity date of the contracts, which represent weakening and
wobbly market interest. The criterion established to identify the
shift from the nearby contract observations to the next nearby
contract is based on when both the daily trading volume and open
interest for the next nearby contract surpass those for the nearby
contract; it is considered as the evidence of shifting market’s
attention from the nearby contract. At this point we roll-over the
series to the next nearby contract. So
we consider the selected 8 Major Agricultural Commodities are taken
as Chana ,Turmeric , Barley,Wheat, Pepper, Soya Bean, Coriander
(Dhanya) and Castor Seed. For the Study by adopting Phillip Perron
and Augmented Dicky-fuller (ADF) to check for stationarity among the
time series data , Johansen’s Co-integrated model to examine the
lead-lag between the 8 selected spot and future agricultural
commodity prices and by using Error Correction Model (ECM) and
Granger causality analysis to examine the linkages among the
agricultural commodity futures and spot prices.