Collateral Value Risk-commodity Linked Exposures

7/30/2019 Collateral Value Risk-commodity Linked Exposures

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UTI BANK

Risk DepartmentCentral Office

CMD:

President (CB):

SVP (SME & Agri):

SVP (Risk):

VP (Risk):

9th

Nov 2005

AGRI BUSINESSMEASURING COLLATERAL VALUE RISK

IN

COMMODITY LINKED EXPOSURES

1. Agribusiness Portfolio:

Our Banks agribusiness portfolio has increased from Rs 1336.65 crs in FY2004 toRs 1590.90 crs FY2005. During the year we have sanctioned/disbursed credit tofarmers for specific crops/commodities under contract farming arrangement andin other cases against warehouse receipts. These exposures have direct orindirect linkage with commodities as they form a substantial part of theagribusiness value chain. At present exposure is spread over commodities such

as castor seeds, Paddy, Maize, cotton, sugarcane etc. Also under SME segmentwhere units are engaged in processing of agricultural produce such as crude palmoil, soya meal etc, and commodity linkages are substantial, volatile commodityprices inflict significant price risk on both direct and indirect commodityexposures. We discuss below briefly the different category of risks that canimpact such exposures.

Commodity risk management 1


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2. Definition of commodity finance (direct exposures):

The Basel II document on capital adequacy under paragraph 224 refers tocommodity finance as short term lending to finance inventories/receivables of

exchange traded commodities such as crude oil, metals or crops where theexposure will be repaid from the proceeds of the sale of commodity and theborrower has no independent capacity to repay the exposure. The structurednature of the financing is designed to compensate for the weak credit quality ofthe borrower.

3. Credit Risk in Commodity exposures :

Dimensions of credit risk differ as between direct/indirect exposures on commodity.

3.1 Risks-Direct exposures (of self liquidating nature):

Price risk: Uncertainty around the value of the commodity bundlecollateralizing the exposure due to volatility in commodityprices/deterioration in quality of stock deposited at the warehouse.

Liquidity Risk: Liquidity risk arises when the commodity bundlecollateralizing the exposure is not saleable within the specified tenor ofthe exposure.

Credit Risk: In self-liquidating short term structured commodityfinance credit risk (defined as probability of default) is driven by theabove two factors.

3.2 Risks- Indirect exposures: (which are not self liquidating)

Indirect lending to commodities encompasses lending for crop production,agriculture processing and other related agriculture activities wherecommodity linkages form substantial part of the value chain.

Production risk: Risks relate to loss in value of produce due to agrarianfactors such as soil types, drought, flood, crop disease, lower landholdings etc.

Price Risk: It is defined as uncertainty in output prices farmers expect torealize and contrary input prices processing units pay for agricultureproduce. The nature of price risk will differ from commodity tocommodity. Price risk is present in case of indirect lending during the pre-

harvesting period as the prices of output as well as various inputs aresubject to volatility.

Financial risk: This refers to the degree of financial leverage of thefarmers/processing units in relation to their cash flows.

Credit Risk: The above factors are the key drivers to a credit riskresulting in loss due to default. .

4.Importance ofManaging Price risks :



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As mentioned above, price risks are significant drivers of credit loss in directcommodity lending as compared to indirect commodity lending. Being self-liquidatingin nature, the repayment capacity of structured commodity lending exposuresdepends upon factoring/managing price risks while taking a credit decision.

Therefore, it is important that banks measure price risks separately and stipulate

adequate margins to factor the same while setting exposure limits.

Price risks/ value at risk associated with specific commodities can be measured usingseveral statistical techniques. It is natural that such risk measurement must becentralized to serve as input to the process of monitoring credit risk of the Bankscommodity exposures/ portfolio.

5. BASEL Perspective on margin requirements:

The Basel document on capital adequacy under paragraph 156 to 160 clearly spellsout the following basic requirements to be met if banks were to internally estimatemargin requirements for collateral (such as commodities) backing their creditexposures.

Volatility estimates should be under 99th percentile confidence interval.

Minimum holding period of the stock to be considered for volatility estimateswill be dependent upon the type of transaction and the frequency of remargining to the market.

Banks may use margin requirements for shorter holding periods scaled up toestimate the margin requirement for longer periods by using the square rootof time formula.

Banks must take into account the liquidity of lower quality assets in whichcase holding period should be adjusted upwards given the relatively lower

liquidity of collateral.

Choice of observation period sample data points for statistical estimation ofmargin requirements shall be minimum one year. Also weighted average timelag of observations cannot be less than 6 mths.

Banks are required to update their databases no less frequently than once inevery three months and should re assess them whenever the market pricesare subject to material changes. Effectively margin requirements forcommodity exposures must be reviewed every three months.

Banks are free to use any model. Examples are historical simulation andMonte Carlo Simulation.



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6. Methodology :

For the purpose of calibration, price risk is defined statistically as volatility inprices.

As a first step we identified few commodities namely Coffee, Castor seeds,

RBD Palmolien, Chilli, Sugar and Soya meal across which our Bank hascurrently exposure both through direct/indirect lending to these commodities.

Historical data on prices spanning at least 100 trading days across the abovecommodities were obtained in order to calibrate the volatility.

Based on the historical data, volatility of commodity prices were measuredusing two fundamental statistical techniques namely equally weightedvariance method and Exponentially Weighted Moving Average Method. Thevolatilities were measured along different tenor such as Daily, Weekly,Monthly, Quarterly, Semi Annually and Annually indicating the risk over

different holding periods. The above two methods are well known and havebeen used by reputed organizations such as J.P Morgan (Risk metrics) foranalyzing volatility of their exposures. Brief idea on the above two methods isprovided in the annexure.

7. Results on Sample data:

On carrying out statistical analysis, we have arrived at the above volatilityestimates for commodities across different time horizons.

Price volatility (in percentage changes) estimated by the basic VAR method acrossdifferent holding periods of commodity stock is as presented below:

Volatility in % as measured by equally weighted variance methodCommodity

Datapoints

Daily Weekly Monthly Quarterly

Semi-Annual

Annual

Coffee 120 1.41 3.73 7.71 13.36 18.89 26.72

CastorSeeds

270 0.89 2.34 4.85 8.40 11.88 16.80

Chilli 120 1.60 4.24 8.78 15.21 21.51 30.42

Soya Meal 300 0.82 2.17 4.49 7.78 11.00 15.55

RBDPalmolien

250 1.18 3.11 6.44 11.15 15.77 22.31

Sugar 300 0.51 1.35 2.79 4.83 6.84 9.67

Volatility estimates in % under EWMA method

Commodity

Datapoints

Daily Weekly Monthly Quarterly

Semi-Annual

Annual

Coffee 120 1.25 3.31 6.86 11.88 16.81 23.77

CastorSeeds

270 0.93 2.47 5.12 8.86 12.53 17.72

Chilli 120 2.14 5.66 11.72 20.30 28.71 40.60

Soya Meal 300 078 2.08 4.30 7.44 10.52 14.88

RBDPalmoilen

250 2.93 7.75 16.04 27.78 39.28 55.55

Sugar 300 0.34 0.90 1.87 3.23 4.57 6.46



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Apparently, the sample size is not adequate for some commodities, as we had todepend upon the price history provided by NCDEX. However with continuousupdating of commodity data, it is possible to enrich the database on price historyresulting in better volatility estimates.

In case of commodity exposures (direct/indirect) backed by forward/futures contracts

on exchanges, margin requirements will be based upon the exchange regulationsand no in-house calibration is required. However in case of un-hedged exposures, theBank on the basis of the volatility of the underlying commodity can estimate marginrequirements. Margin requirements will depend upon the mark to market periodchosen by the Bank. For instance, assuming an exposure to Chilli and monthlymarking-to-market period, initial margin should atleast be (2.32 * 11.72%) = 27.26%to cover losses in 99% of cases over a period of one month. The factor 2.32 relates to2.32 standard deviations at 99% confidence intervals. At the end of one month,exposure should be marked-to-market again and margin call, if required, be given tothe customer to replenish the margin account.

8. Suggested Risk management measures :

Given the Banks exposure in commodity lending and BASEL requirements onmanaging credit/market risk associated with collaterals, the following action pointsare suggested to sensitize decision making in respect of commodity exposures. : -

a) Quarterly Management Information system on the following to be regularized on aquarterly basis: -

Identifying and reporting commodity exposures.

Estimating and revising margin requirements across commodity exposures.

Identifying short fall in margin money in existing exposures and triggeringmargin calls.

Recommended actions in case of default in margin money requirements.

b) As adequate time series and continuous data on commodity price history will berequired for volatility estimates, it is preferable to purchase commercial data

bases available if any, on price history, to complement the data downloadablefrom commodity exchanges. Also adequate software to be developed/purchasedin order to mine the data base, compute volatility estimates, map the volatility toa specific term structure, flag extreme volatilities and also provide enoughflexibility for additional analysis.



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c) Although delivery of credit on commodity based lending is through branchnetwork, it is felt preferable to centralize the management of commodity riskat the central office. Initially, weekly/monthly marking-to-market can be done

to measure the value at risk and branches/zonal offices /credit department atcentral office be updated for onward action.

d) With increase in the depth of commodity database, the horizon over which theprice risk is observed can be extended beyond one year.

The above initiatives will not only help in pricing risks based on volatility estimatesbut also enable to discover opportunities for improved lending to commoditysegment.

Submitted for information

Krishnan Chari Dinesh ChaudharyAsst Vice President( Risk ) Dy Manager( Risk )



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Annexure 1

A brief technical note on statistical method used for estimating price volatility

To estimate the volatility of each commodity empirically, commodity prices are

observed at fixed intervals of time like daily, weekly, monthly etc. In this case, we

have taken daily commodity prices available from NCDEX. Daily volatility is the

standard deviation of daily returns of commodity prices. Therefore, the steps

involved are:

1. Daily percentage changes in commodity prices are calculated using the formula:

ui = Ln (Si / Si-1)

where

Si: Commodity prices at end of ith (i = 1, 2, 3..n) day

Ln: Natural logarithm

ui: Daily return of a commodity

However, all the data points were not spaced over 1 day because of market closure

on weekends and holidays. One must take into account weekends and other non-

trading days as the price changes over these periods signify return for more than a

single day. As we are assuming that the spot price of the commodity follows a

geometric Brownian motion, this could be done simply by dividing log price changes

by the square root of the number of intervening days (e.g., three days in the case of

a week-end), and then calculating the sample variance.

2. Calculation of daily volatility

Daily volatility, s, of a commodity is calculated by taking the standard deviation of all

ui where i = 1,2,3n. Daily volatility can then be scaled to n-day volatility by

using the formula:

n-day Volatility = Daily volatility * n

The scaling factor of n appears because we assume commodity prices to follow

Markow process. In case of Markow processes, the past history of a variables and the

way the present has emerged from the past are irrelevant. When Markow processes

are considered, variance in successive time periods are additive. This is because



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change in two periods is the sum of two independent normal distributions. When two

independent normal distributions are added, the result is a normal distribution with

variance equal to sum of the variances of two distributions.

Therefore, 7-day variance is 7 times 1-day variance added together. As a result, 7-

day standard deviation is equal to:1-day variance * 7 = 1-day standard deviation * 7

Choosing n, number of observations, is generally set equal to the number of days to

which volatility is to be applied. Therefore, for our purpose, we should be using

observations of last 180-360 days.

The above method gives equal weight to all ui. If our objective is to estimate the

current level of volatility, we can give more weight to recent data. For this we can

use EWMA method.

Exponentially Weighted Moving Average

In EWMA method, less weight is given to older ui. According to the model,

2n = 2n-1 + (1- ) u2n-1

The estimate of the volatility for day n is calculated from n-1 (the volatility estimate

for day n-1) and un-1 (the most recent daily return in commodity prices).

The value of governs the responsiveness of the current estimate to the most recent

daily return in commodity prices. A low value of assigns greater weight being given

to recent returns in commodity prices. In this case, estimates produced for volatility

on successive days are highly volatile. A higher value of (a value close to 1)

produces estimates of the daily volatility that respond slowly to new information

provided by the daily returns.

In this case, we have used = 0.94. This is the value used by the J.P. Morgans Risk

Metrics database. The company found that across a range of different market

variables, this value of gives forecasts of the volatility that come closest to realized

volatility.


Documents

Collateral Value Risk-commodity Linked Exposures