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Weather Market in India

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Consultants. OTC. Exchange. Insurers. Banks. Energy Companies ... Quantos, Satellite Image weather indices, Weather-Area yield. Cat indices. Weather Risk ... – PowerPoint PPT presentation

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Title: Weather Market in India


1
Weather Market in India
  • Weather Risk Management Services Pvt. Ltd.

2
Agenda
  • Background
  • Examples of Weather Insurance in India
  • Issues
  • Initiatives

3
Weather Markets The Need
  • Farmers
  • Agriculture credit off-take in ninth plan Rs.
    2,31,798 crores (grew _at_ 20 pa) Target for X
    plan Rs. 7,36,570 crores
  • 90 crop losses on account of weather related
    risks
  • Rural Economy is highly weather dependent
  • Limited success of area yield based crop
    insurance
  • Commodity Traders
  • Weather related supply bottlenecks make dry-land
    commodities very volatile
  • Intraday volatility of Guar, chilly touches
    10-15 (daily trading at national exchanges
    touches Rs.1000 crore daily)
  • Vegetable and fruit Mandis highly dependent on
    temperature (Delhi Mandi trade alone touches
    Rs.1000 crore annually)
  • Trader income dependent on weather vagaries
  • Industries like agro-input companies, food
    processing industry, companies, plantations,
    FMCG, Banks, Power sector etc
  • Not uncommon to find Agri-Input companies, whose
    sale dips by over 30-40 due to fluctuation in
    rainfall

4
Weather Markets The Need
5
Weather Markets The Need
  • EID Parry sales, net down 86 pc on monsoon
    failure. - The Hindu, Jan 17, 2003
  • The Company's business is seasonal in nature and
    the performance can be impacted by weather
    conditions - Notes to Accounts, Syngenta (I) Ltd.
  • Monsanto India continued its strong profit growth
    on the back of positive all-round business
    performance aided by a good monsoon. - Annual
    Report 2003-04, Monsanto Ltd
  • The delayed monsoon has hit the fertilizer stocks
    badly. - Analyst, Hindu Business Line
  • Over 1000 farmers commit suicide in vidarbha and
    Telangana in last two years TOI
  • An average drought costs upto Rs 4 bn to the
    state exchequer,Gujarat earthquake resulted in
    direct damages of about Rs.153 billion -NDMC

6
Agri Risk Management Current Scenario
  • Crop Insurance
  • Delay in Claim Settlement
  • High Cost of Risk Transfer, Inefficient
    allocation of public resources
  • Premium to claim 15
  • Reserves in the order of Rs.8000-10000 crores
    required
  • Lop-sided claim settlement mechanism with high
    possibility of subjective bias
  • Less than 2 farmers taking insurance on a
    voluntary basis
  • About 12 of loanee farmers taking insurance
  • Doesnt address the risk management requirements
    of non-farmers
  • To a large extent the risk is weather dependent
  • In absence of meaningful risk management product
    --Agriculture production is sub-optimized
  • Inefficient distribution of agricultural inputs
  • Large tracts of land unused/ used for
    non-commercial crops

7
Why Weather Market
  • Agricultural loss in many parts of the country is
    weather dependent
  • Weather Insurance/Derivatives can fill in the gap
  • Loss can be monitored real time
  • Cost of risk transfer can be reduced through
    weather trading
  • Weather (esp. rainfall) is the common commodity
    across diverse agri-products, industries
  • Explains up-to large variation in prices for
    commodities in the dry land
  • Entities on both the long and short side

8
Why Weather Market
  • A diverse set of participants exchanging risks
    shall reduce cost of risk transfer
  • Volatility is comparable to other commodities
    such as Gold, agri-commodities
  • Attracts speculators and trading community
  • Synergies with Energy Market
  • Power deregulation would further strengthen the
    synergies
  • India has the potential to emerge as the largest
    weather market
  • An Indian Company/Exchange can lead the world
    market

9
Weather Market Instruments
  • Weather Insurance
  • Assurance against losses due to a specified
    weather event
  • Loss compensation based on a pre-agreed formula
  • Weather Derivatives
  • Option
  • Futures
  • Bonds
  • Bond with payments triggered/calculated on basis
    of a weather event

10
India is looking for such Instruments
  • Financial solutions to go along with technical
    solutions
  • Leveraging inherent diversification and non
    uniform impact of weather events
  • Informal rainfall markets exist
  • Weather Insurance
  • Launched in 2003, approved by IRDA
  • Weather futures and option trading to be allowed
    soon
  • Commodity futures market reformed in 2000
  • Since then an exponential growth in future
    trading witnessed
  • Synergies with power, carbon markets
  • Weather bonds being contemplated by Institutions

11
Why Weather Market
  • Linkage of the underlying with economy is
    important
  • Ensures buyers Sellers
  • Base liquidity further deepens the market
  • Weather impacts approx.
  • GDP of 150 bn in India
  • GDP of 200-250 bn in India China
  • GDP of 400-450 bn in top 8 developing economies

The economist 2005, 2006
12
The Eventual Market
  • Coming together to create a market of approx. S
    0.6 tr by 2020

13
Structure of the market
14
Agenda
  • Background
  • Examples of Weather Insurance in India
  • Issues
  • Initiatives

15
Issues
  • No historical precedence
  • No active cash/spot markets
  • No way to quantify impact of weather
  • No way to price weather for different locations
    (any given geographic longitude-latitude)
  • Large distances
  • Concept of weather based risk management is new
    and abstract
  • No secondary market to supplement it
  • Regulatory Issues
  • Financial product penetration in agri-market is
    shallow

16
Issues
  • Existing Weather Insurance
  • High Basis Risk
  • Inadequate Weather Station Coverage
  • Inaccurate cover design
  • Difficult for the insured to envisage cover
    benefits
  • High Cost of Risk Transfer

17
Basis Risk Impact
18
Current Weather Risk Management Practice
Unable to reduce cost of risk transfer by
diversification in a covariate environment
  • Lesser or no attention to
  • Accurate risk identification
  • Inherent risk management possibilities
  • Proper monitoring for risk minimization
  • Inherent basis risk
  • -Inadequate shorters

Outcome is high cost of risk transfer and failed
risk management program
19
Agenda
  • Background
  • Examples of Weather Insurance in India
  • Issues
  • Initiatives

20
Our Initiatives
  • Create the much needed Cash/spot market

Real Time Data availability for any given
long-lat
Decision Support system(s) for major customer
segments
Platform enabling Trading and cost reduction
Marketing Network/Relationships
  • Build active futures/option trading market

21
Our Initiatives
  • Deepening the Primary market
  • Technology development
  • Resolving the key constraints
  • Developing the secondary market in tandem
  • Launching the Indices for key regions
  • Approaching the key market segments
  • Commodity funds, Agri-funds, Rainfall
    speculators, International trading funds
  • Push for regulations on participation by Banks
    and MFIs
  • Presence in both the OTC and exchange traded
    market
  • Developing the Hybrid market
  • Quantos, Satellite Image weather indices,
    Weather-Area yield
  • Cat indices

22
Organizing Real Time Data
  • Covering important agricultural zones real time,
    at a cost of approx. Rs.500 per sq.km or Rs.5 per
    ha
  • Generation of Historical records for any given
    long.- lat. Position
  • Statistical Neural Network model

23
computer vision/aerial imaging
  • Mosaic generated from images taken from UAV of
    IITK campus (Courtsey Aurora Integrated Systems)

aerial image mosaicing
aerial image registration
24
Agro based applications of UAV
Hyperspectral Remote Sensing Indices for Crop
Status
  • Aerial imaging has been identified as the
    foremost technique to generate agronomy indices
  • EO sensors along with a FLIR sensor provide
    accurate estimation of crop/vegetation growth and
    health
  • Addition of hyper-spectral sensors provide
    information that as of now exists only through
    low resolution satellite data
  • UAVs flying at various altitudes can provide very
    high resolution images as compared to satellite
    data
  • Within-field variability of yield monitor spatial
    data can be collected during harvest and
    correlated with hyperspectral indices related to
    crop growth and canopy structure, chlorophyll
    concentration, and water content.

25
Spatial Interpolation
RMS error ( under square root transformation)
2mm
Neural network, AI (HMM) models to improve
precision
26
Decision Support Architecture
Plugging out of risk
Transfer
Forecast
Monitoring
Quantification
Establishing Existence of Risk
Identification
27
Calculating Stage-wise Water Imbalance
Water Surplus / (Deficit)
Water Requirement
Water Availability
  • Depends on
  • Total Rainfall
  • Soil Conditions
  • Depends on
  • Crop Physiology
  • Max Min Temperature
  • Sunshine Hours
  • Relative Humidity
  • Soil Condition is specific to a given area
  • Water imbalance at each stage has different
    impact on yield
  • Water imbalance can be scientifically modeled
  • Different model required for each crop location

28
Water Imbalance Yield Impact
29
Phase-wise Impact of Weather RiskE.g. of Paddy
Crop
1.2
0.4
0.2
Yield Response Factor to water imbalance. Higher
response factor means higher impact on yield due
to water imbalance
30
Modeling Pest Extreme Weather Risks
  • Identifying yield losses on account pest and
    disease accelerating weather conditions
  • For e.g. rainy conditions with conducive
    temperature humidity cause extreme elongation
    of downy mildew in Grapes
  • Preparing catastrophe risk models to identify and
    evaluate losses on account of extreme weather
    conditions
  • E.g.Thunderstorm and Flood modeling
  • Economic models for other participants in the
    agricultural chain
  • Take into account other factors such as
    logistics, existing risk management mechanisms

31
Decision Support System
Forecast Inputs
  • Crop Planning
  • Irrigation planning
  • Pest Management
  • Yield forecast
  • Risk Transfer

Front-end Output
Identification Quantification of the Risk
Scientific modeling of risk
Simplified output in vernacular language for
farmers
Current Weather Data
Needs to be customized for Banks portfolio
32
At Village Service Point
Special package for Rainwater harvesting
project
33
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35
Trading Platform
  • Trading possible on any given long-lat
  • Guided by DSS
  • Embedded Allocation algorithm
  • Supported by market making funds
  • Available for both OTC, traded futures options

SWAP
Futures Exchange
Futures Exchange
36
Facilitating Linkages
Premium reduction possibilities due to hedging in
trading market
Distribution Outreach
Trading Market
Agri-trader / Speculator
Banks
Weather Futures Trading
Exchange traded options/Insurance
Village level service points
Trading Fund
Input providers to be roped in to share premiums
  • The network to be established across dry-land
    regions of the country
  • Prominent FII trading funds (re-insurers being
    roped in)
  • Exploring geographical diversification
  • Targeting/identifying the sellers in the weather
    market
  • Link with the trading market
  • Explore the OTC market

37
Developing the Hybrid Market
Hybrid Area yield -weather
Quantos, hybrid Weather-satellite images
weather rainfall, temp, humidity,
38
Our Role
Technology Service Provider
39
Thank you
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