Role of Indian Commodity Derivatives Market in Hedging Price Risk: Estimation of
Dynamic Hedge Ratio using Multivariate GARCH Model
GARP Delhi, India Chapter Meeting
Brajesh Kumar Jindal Global Business
School
Outline Of The Presentation
Futures market and Hedging Recent dynamics in Indian spot and
commodity derivatives market Research Questions Models Constant hedge models Dynamic hedge models
Findings and Conclusions
Futures and Commodity Futures Agreement to buy or sell an asset for a certain price at
a certain time
Commodity futures are different Storage cost and convenience yield Contango/Backwardation
Corn Farmer
Company
Contract to buy/sell fixed quantity of
Corn at fixed price
Settlement will take place
250
270
290
310
330
350
370
390
20-Oct-04 19-Nov-04 19-Dec-04 18-Jan-05 17-Feb-05 19-Mar-05 18-Apr-05 18-May-05 17-Jun-05 17-Jul-05 16-Aug-05 15-Sep-05 15-Oct-05 14-Nov-05 14-Dec-05
SPOT PRICEJAN FutureFEB FutureMAR FutureSEP FutureOCT FutureNOV FutureDEC Future
Futures Prices < Spot price(Backwardation)
Futures Prices >Spot price(Contango)
ARRIVAL LEAN Period PLANTATION
4
Illustration: Castor Spot And Futures Prices
Role of Commodity Futures Markets
Importance and management of Commodity Market Management through spot market intervention Management through Derivatives market
Management through derivatives market Price discovery Risk management Investment
Hedge Ratio and Hedging Effectiveness
Basic Idea To compensate loss/ profit in one market by profit/loss
in other market. In portfolio theory, hedging with futures can be
considered as a portfolio selection problem to minimize the overall risk.
The optimal hedge ratio is defined as the ratio of the size of position taken in the futures market to the size of the cash position which minimizes the total risk of portfolio
Hedge Ratio and Hedging Effectiveness
Return
Variance
Minimization of variance give OHR
Hedge Effectiveness
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Commodity Derivatives Market In India
Year 1875 Derivatives market was started with the setting up of the Bombay Cotton
Trade Association
Year 1952 & 1953 Forward Contracts (Regulation) Act (FCRA) Forward Market Commission (FMC)
Year 1955 Free trade in many commodities was restricted
Year 2003 Removed the ban on futures trading Three national level multi commodity exchanges, National Multi
Commodity Exchange (NMCE), Multi Commodity Exchange (MCX) and National Commodities and Derivatives Exchange (NCDEX) were setup in 2004.
Recent Dynamics
Expansionary measures Planning to increase participation though Foreign Institutional
Investors (FIIs), Mutual Funds (MFs) and Banks Planning to introduce other derivatives products like futures
options, index, index, index futures & options etc Commodity transaction tax (CTT)
Critiques Govt. of India decided to suspend the futures trading in urad,
tur and wheat in early 2007. Abhijit Sen committee: to study the impact of futures trading
on agricultural commodity prices. In May 2008, Govt. of India banned futures trading on another
four commodities namely rubber, chana, soy oil and potato.
10
Some Facts About Indian Commodity Futures Markets
Reference settlement spot price of Industrial metals and Energy commodities are LME and NYMEX Spot price
Most of the non-agricultural commodities are cash settled contracts
Speculation ratio of non-agricultural commodities are very high compared to agricultural commodities
Overnight volatility of non-agricultural commodities are very high as compared to trading volatility.
Volume of near month futures is very high as compared to next to near month futures contracts for non-agricultural commodities
Motivation
Hedging effectiveness Found to be contingent on model used to estimate hedge ratio Few Studies in Indian commodity futures
Why it is important to look into? Would help in understanding effectiveness of commodity
futures contracts in managing risk may also help concerned exchanges and the government to
devise better risk management tools or support towards commodity-specific public policy objectives
Aggregation model Would help participants (farmers) in devising better hedging
strategies
Research Questions
To investigate optimal hedge ratio and hedging effectiveness of select futures contracts from Indian commodity markets.
To examine performance of constant hedge
ratio models (VECM) and dynamic hedge ratio models (CCC-Multivariate GARCH) in emerging market context
Models: Constant Hedge Models
OLS
VAR
VECM
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Models: Dynamic hedge models CCC-MGARCH
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Constant conditional correlation (CCC) model Bollerslev et al.(1990)
fft
sftt h
hH =Dynamic Hedge Ratio
Details of Commodity, Data Period and Source
Commodity Data-Period Futures Market
Spot Market
Agricultural
Soybean 09/01/2004 to 10/20/2008 NCDEX Indore
Maize 01/05/2005 to 10/20/2008 NCDEX Nizamabad
Castor Seed 09/21/2004 to 10/20/2008 NCDEX Disa
Guar Seed 04/12/2004 to 09/19/2008 NCDEX Jodhapur
Precious Metals
Gold 05/02/2005 to 09/30/2008 MCX Ahmedabad
Silver 05/02/2005 to 09/30/2008 MCX Ahmedabad
Industrial Metals
Aluminium 02/01/2006 to 09/30/2008 MCX LME
Copper 07/04/2005 to 11/20/2008 MCX LME
Zinc 04/03/2006 to 09/30/2008 MCX LME
Energy Crude Oil 05/02/2005 to 09/30/2008 MCX NYMEX
Natural Gas 07/21/2006 to 09/30/2008 MCX NYMEX
Unit Root and Johansen Co-integration Tests
ADF Test Prices are Non-stationary Returns are stationary
Co-integration Spot-Futures prices are co-integrated Exceptions
Guar seed: Near month (second sub-period) Natural gas: Next to near month (Entire and both the sub-
periods) Maize, Aluminium, and Copper : next to near month (first sub-
period) Castor, and Crude : next to near month (Second sub-period)
Hedge Ratio and Hedging Effectiveness: VECM
2004-2008 2004-2006 2007-2008
Commodity Hedge Ratio
Hedging Effectiveness
Hedge Ratio
Hedging Effectiveness
Hedge Ratio
Hedging Effectiveness
Soy Bean 0.739833 0.692946 0.731624 0.702977 0.735484 0.689706
Maize 0.317607 0.237123 0.333731 0.183017 0.315907 0.338375
Castor Seed 0.584379 0.486313 0.516682 0.398208 0.670206 0.598378
Guar Seed 0.477391 0.437719 0.496459 0.454555 0.395943 0.398083
Gold 0.316849 0.236243 0.32528 0.225757 0.313358 0.24966
Silver 0.232021 0.146975 0.237217 0.138 0.242272 0.180167
Aluminium 0.262816 0.096287 0.1625 0.045788 0.38085 0.225528
Copper 0.097611 0.027994 0.111726 0.024454 0.114104 0.061162
Zinc 0.295546 0.197039 0.200687 0.092126 0.344876 0.270389
Crude Oil 0.349902 0.189441 0.432523 0.234767 0.276419 0.122934
Natural Gas 0.409389 0.189156 0.397671 0.222604 0.414326 0.198484
Near Month Futures
Hedge Ratio and Hedging Effectiveness: VECM
Next To near month Near Month Futures
2004-2008 2004-2006 2007-2008
Commodity Hedge Ratio
Hedging Effectiveness
Hedge Ratio
Hedging Effectiveness
Hedge Ratio
Hedging Effectiveness
Soy Bean 0.577326 0.311315 0.694536 0.393548 0.496836 0.267845
Maize 0.242724 0.133362 0.233463 0.062189 0.286811 0.287731
Castor Seed 0.542966 0.452535 0.496621 0.370665 0.271338 0.096645
Guar Seed 0.438871 0.430664 0.452964 0.443269 0.327935 0.31789
Gold 0.342242 0.246231 0.375518 0.256074 0.333604 0.240686
Silver 0.245991 0.150156 0.244549 0.137873 0.269838 0.19757
Aluminium 0.273305 0.09171 0.01866 0.000515 0.434189 0.24516
Copper 0.120029 0.032746 0.102194 0.016025 0.116117 0.05452
Zinc 0.299099 0.170797 0.167391 0.054529 0.365703 0.266877
Crude Oil 0.379611 0.185514 0.48726 0.231297 0.161897 -0.024979
Natural Gas 0.354383 0.08307 0.028244 0.012088 0.21348 0.027981
Dynamic Hedge Ratios: CCC
Commodity Hedge Ratio
Hedging Effectiveness, E
Hedge Ratio
Hedging Effectiveness, E
Near Month
Futures Next to Near
Months Futures
Soy Bean 0.4276 0.6335 0.4521 0.4707
Maize 0.892 0.2418 0.4046 0.1514
Castor Seed 0.7126 0.4917 0.5329 0.4577
Guar Seed 0.5022 0.4367 0.4063 0.4316
Gold 0.2723 0.2407 0.2698 0.2492
Silver 0.2681 0.1385 0.229 0.1459
Aluminium 0.5048 0.1481 0.4518 0.1449
Copper 0.1072 0.0402 0.1103 0.0468
Zinc 0.2795 0.1972 0.363 0.178
Crude Oil 0.3951 0.1978 0.3869 0.192
Natural Gas 0.3849 0.2486 0.4472 0.1446
Empirical Findings
Agricultural commodities and precious metals, futures contracts provide higher hedging effectiveness as compared to other non-agricultural commodities
Hedging effectiveness provided by the near month futures is
higher than the next to near month futures for most of the commodities.
For most of the commodities hedging effectiveness has
improved in the recent sub-period. Hedge ratios and hedging effectiveness estimated from CCC-
GARCH model are not very different
Further Investigation: Industrial Metals and Energy Commodities
Indian futures markets of industrial metals and energy commodities may aggregate global price information from LME/NYMEX futures markets than spot prices.
Most of the futures contracts of non-agricultural commodities are cash settled contracts than actual delivery
LME/NYMEX futures also provide similar hedging effectiveness with their spot prices
Further Investigation: Industrial Metals and Energy Commodities
India Futures and Int Futures Int Futures and Spot
Commodity Hedge Ratio
Hedging Effectiveness
Hedge Ratio
Hedging Effectiveness
Aluminium 0.552 0.29829 0.60376 0.54981
Copper 0.56684 0.41952 0.68964 0.56625
Zinc 0.64848 0.51193 0.62216 0.63114
Crude Oil 0.81525 0.45 0.34978 0.19862
Natural Gas 0.79289 0.71676 0.43591 0.25234
Conclusions Differences between agricultural and non-agricultural commodities
with regard to the hedging performance
Agricultural commodities: hedging effectiveness (30-70%) industrial metals and energy commodities (less than 20%) LME futures are providing good hedging effectiveness with spot prices
Indian commodity futures markets with the LME/NYMEX futures prices: hedging effectiveness of Indian futures contracts increases dramatically.
Similar Hedging effectiveness with constant hedge ratio or dynamic hedge ratios.
The near month futures provide higher hedging effectiveness than next to near month futures.
The hedging role of Indian commodity futures markets has increased in the recent period with increased activity in the market.