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大多数公司通常会接触到各种金融价格波动,作为他们操作的一个天然副产品。金融价格包括汇率、利率、商品价格和股票价格。这些价格变化在公布业绩时的影响有时是巨大的。通常,你会听到公司说在他们的财务报表中,收入降低了大宗商品价格也下跌,或者他们享有意外获得的利润,归因于印度卢比的下降
套期保值是可以减少此类风险的方法之一。如果企业了解套期保值的真正好处那么经纪公司可以产生良好的业务。但对冲不是都通用的。不同的企业面临不同的风险,他们经历的支付周期不同,那么决定了所需的商品和商业周期的运作。本文将解释经纪公司如何通过了解他们的业务为企业提供专属定制的对冲策略。
为了捍卫对冲策略,衡量对冲大宗商品与非对冲大宗商品的有效性是很重要的。风险价值(VaR)就是这样一种我们可以比较性能的方法。
VaR可以用来找出在正常的市场波动中最低价格的商品。这个最小值可以用来告知客户在他的立场近似最大MTM保证金要求。
设计企业的大宗商品对冲策略-Designing Commodities Hedging Strategy For Corporates
Most Corporations generally have exposure to fluctuations in all kinds of financial prices, as a natural by-product of their operations. Financial prices include foreign exchange rates, interest rates, commodity prices and equity prices. The effect of changes in these prices on reported earnings can sometimes be overwhelming. Often, you will hear companies say in their financial statements that their income was reduced by falling commodity prices or that they enjoyed a windfall gain in profit attributable to the decline of the Indian Rupees.
Hedging can be one of the methods of reduction in such risks. Broking firms can generate good business if Corporates understand the real benefit of hedging. But hedging is not very generic. Different firms are exposed to different kind of risks, the payout cycle experienced by them vary, so does the commodity required and the side of business-cycle one operates in. This paper will explain how broking firms can provide custom-made hedging strategies to the Corporates by understanding their business.
In order to defend Hedging strategy, its important to measure the effectiveness of the Hedged commodity against a non – hedged one. Value At Risk (VaR) is one way we can compare such performance.
VaR can be used to find out what can be the minimum price of the commodity under normal market movements. This minimum value can be used to inform a client about the approximate maximum MTM margin requirements on his position.
Also when people invest, they wish to know the amount of risk involved in any particular commodity so that they can choose which commodity to trade in. It’s important for a broking house to convey the risk involved, but conveying a theoretical notion of risk to clients may become impossible without some means to quantify risk. VaR is a very important tool in this regard. This paper attempts to implement VaR in Indian commodity market scenario to address both the above issues.
对冲:(为什么去对冲和谁应该对冲?)-Hedging :
( Why go for Hedge and Who should Hedge?)
For firms / individual who have an exposure to a particular commodity (either he is a buyer or a producer of it), hedging with futures contracts is one of the most widely used techniques for managing risk. Hedgers who face price uncertainty aim to manage their exposure to adverse movements in spot prices by taking an opposite position in the futures market so that losses in one market is offset by gains in the other.
Initial opinion about hedging can be traced back to Marshall (1919) who expressed that hedging is not speculation but insurance. Keynes (1930), famous for his work in economics, also stated that hedging is used as a means of avoiding risk. The main purpose of hedging is the desire to stabilize income and increase expected profits (Kamara (1982))
One reason why companies attempt to hedge is because they wish to remove risks that are peripheral to the central business. For example, a electrical copper wire manufacturing firm is known for its quality wires. It’s a natural belief that their profits will be driven by the quality of their produce and how well they can market the product and that they apparently do not have exposure to financial/commodity price risk. But that’s a misconception. This company faces a huge amount of price risk due to Copper price variation. Dealing with copper price is not their core business, so they would naturally want to remove any risk arising due to it. Need for hedging is as natural as need for insurance against theft or fire.
Another reason for hedging the exposure of the firm to its financial price risk is to improve or maintain the competitiveness of the firm. Companies do not exist in isolation. They compete with other domestic companies in their sector and with companies located in other countries that produce similar goods for sale in the global marketplace. If there are five companies in a particular sector and three of them engage in a comprehensive financial risk management program, then that places substantial pressure on the more passive companies to become more advanced in risk management or face the possibility of being priced out of some important markets. Firms that have good risk management programs can use this stability to reduce their cost of funding or to lower their prices in markets that are deemed to be strategic and essential to the future progress of their companies.
Setting hedging policy is thus a strategic decision, the success or failure of which can sometimes make or break a firm. The core problem when deciding upon a hedging policy is to strike a balance between uncertainty and the risk of opportunity loss. But, hedging is not a simple exercise nor is it a concept that is easy to pin down. Hedging objectives vary widely from firm to firm, even though it appears to be a fairly standard problem, on the face of it. And the spectrum of hedging instruments available to the corporate Treasurer is becoming more complex every day. Hedging is also contingent on the risk-preferences of the firm's shareholders. There are companies whose shareholders refuse to take anything that appears to be financial price risk while there are other companies whose shareholders have a more worldly view of such things. It is easy to imagine two companies operating in the same sector with the same exposure to fluctuations in financial prices that conduct completely different policy, purely by virtue of the differences in their shareholders' attitude towards risk.
But people generally believe investing in futures is only done for trading interest – especially in India commodity hedging is still not used extensively. Futures market is generally considered as a speculative market, but commodities futures market are extremely helpful in hedging also. ( Roger W Grey and David J.S. Rutledge ). In order to educate people, broking houses themselves need to be able to give correct hedging solutions to business. But different companies have different requirements; their payout cycles vary so does the side of the business cycle they operate in. Hence most corporates require custom – made hedging strategies.
First we need to look into the specific commodity the firm is exposed to. In this regard the easiest thing to do is perfect hedge.
完美的对冲-Perfect Hedge:
This is the simplest hedging strategy where you simply take an equal and opposite position in the futures market. This is possible only if there is a futures contract that exactly matches, with respect to the nature of the asset and the terms of delivery, the obligation that is being hedged.
示例1:价格上涨-Example 1: Rising Prices:
A particular firm requires 5 lots of Aluminium in Sept. i.e. it has to buy 25000 Kgs of Aluminium in Sep from the spot market, basis for which will be MCX Price for Sep .
On a particular day following are the futures prices:
To meet this requirement the company has two options available
Procure the commodity from Spot
Take an equivalent position in futures market
If the company decides to procure now
He has to bear the Cost of carry
Uncertainty of prices in futures
What if not hedged: The firm faces uncertainty of prices. Increase in spot prices may affect the company’s future cash outflows
完美的对冲-Going for perfect hedging:
So, in Futures: It sells 5 lots of MCX Sep futures @ Rs. 89=> Rs.10 ‘gain’
So, in Spot : Effects purchase of 25000 Kgs Aluminium @ 89=> Rs.10 ‘loss’ ( Than if he had procured the entire 5Lots on 18th August itself.
Therefore the net effect is: Net of Futures (+Rs.10) and Physical (-Rs.10) = Rs.0
I.e. Has achieved effective Aluminium input price of Rs.79, even though prices rose by Rs.10
示例2:价格下跌-Example 2: Falling Prices
On a particular day following are the futures prices:
A particular firm requires 5 lots of Aluminium in Sept. i.e. it has to buy 25000 Kgs of Aluminium in Sep from the spot market, basis for which will be MCX Price for Sep .
To meet this requirement the company has two options available
Procure the commodity from Spot
Take an equivalent position in futures market
If the company decides to procure now
He has to bear the Cost of carry
Uncertainty of prices in futures
What if not hedged: The firm faces uncertainty of prices. Increase in spot prices may affect the company’s future cash outflows
完美的对冲-Going for perfect hedging:
Buys 5 lots (5 x 5000 Kgs) of MCX Sep futures @ Rs. 99
Later in September, following are the futures prices:
So, in Futures : It sells 5 lots of MCX Sep futures @ Rs.89=> Rs.10 ‘loss’
So, in Spot : Effects purchase of 25000 Kgs Aluminium @ 89=> Rs.10 ‘profit’ ( Than if he had procured the entire 5Lots on 18th August itself.
Therefore the net effect is: Net of Futures (-Rs.10) and Physical (+Rs.10) = Rs.0
I.e. Has achieved effective Aluminium input price of Rs.99, even though prices fell by Rs.10
***
In practice, hedging is often not quite as straightforward. Hence it is not always possible to form a perfect hedge using futures .Some of the reasons is as follows:
The asset whose price is to be hedged may not be exactly the same as the asset underlying the futures contract.
The hedger may be uncertain as to the exact date when the asset will be bought or sold.
The futures delivery dates may not match the asset obligation date
The hedge may require the futures contract to be closed out well before its expiration date or in some cases the contract might be requires to be rolled over multiple times in order to meet the physical requirement date – This leads to short term cash flow problems.
The amount of physical asset required might not be an integral multiple of the existing contract size
There may be a lack of liquidity in the futures market.
One measure of the lack of hedging perfection is the basis risk. Basis is defined as:
Basis = Spot Price – Futures Price of the asset
最小方差对冲-Minimum Variance Hedge
Thus in cases mentioned above, need to find way to use sub-optimal contracts, contracts that are highly correlated with the underlying asset and who have a similar variance.
One common method of hedging in the presence of basis risk is the minimum variance hedge.
In this we need to find out how many lots of the futures contract one needs to buy of the non similar commodity. In order to get this, the following calculation has to be done :
事例-Example:
Suppose a XYZ firm needs Aluminum which is around 85% pure for its wire manufacturing unit. XYZ fears that the price of Aluminum will rise and hence wishes to hedge against future price changes in aluminum. But the one available in the futures exchange is 99.7% pure.
Since the aluminum available in futures is not suitable perfect hedge, they would use the result given (8) above to calculate the optimal number of contracts to purchase in futures.
Calculations for the scenario is attached along in filename << Al Hedging.xlsx >>
As a next step, the exact payout cycle of the corporate is analyzed. Also it is ascertained whether he is a buyer of the commodity or a seller i.e. whether he wishes to hedge on the buy side or the sell side.
In order to give a clearer picture, this paper would try to explain hedging strategies with a few of the most commonly occurring business scenarios. In all these cases its presumed that the initial hedge ratio calculation is done first and then the hedging strategy suggested is followed.#p#分页标题#e#
案件-CASE:
A firm requires 60 MT copper at the end of the month. He takes delivery from the spot market but the firm has a arrangement with the spot dealers wherein the firm does not have to pay the spot price of that day – rather he settles the deal at the average spot price for that entire month. Thus he can avoid any sudden spike in prices on the delivery date.
以套期保值策略为例-Hedging Strategy for CASE:
In this case the hedging has to ensure that the futures price mirror the kind of average price he achieves in spot only then can the hedge be close to perfect hedge.
Initially hedge ratio is calculated between the spot prices of Copper and the prices of futures. This gives number of lots of the futures contract one needs to buy in the futures market.
In this case the hedging has to ensure that the futures price mirror the kind of average price he achieves in spot.
For this the firm buys the entire 60 MT initially in futures.
If there are n more trading days left for the month, the firm can sell (60/n) lots of Copper per trading day. This way the firm will be able to achieve the average of the futures price. In this strategy too, either, the spot transaction will result in profit and the futures will end in loss, or vice versa – thus providing a hedge.
In this strategy too, either, the spot transaction will result in profit and the futures will end in loss, or vice versa – thus providing a hedge.
The detailed calculation on the above scenario has been done for the entire financial year 2009-10.
The file << Cu Hedging.xlsx >> is available in the annexure.
解释-Explanation:
The firm buys the entire 60 lots (300,000 Kg) on the first day at the best price available for the day (average of high and low for the day) – This becomes his “Total Purchase Value”
From the next trading day onwards, the firm sells (60/no of trading days) lots of Copper futures again at the best price.
Sum of these selling values form the “Total Sale Value”.
Net Profit for the month =Total Sale Value - Total Purchase Value
With this strategy the prices at which he buys at futures will mirror the kind of prices he achieves using averaging over a month in spot. Hence even in this scenario, he can successfully achieve close to perfect hedge.
Thus if the firm had continued to procure copper from the spot only, they would have made a net loss of Rs 43.5 lakhs. But since the futures transaction gives a reverse effect, the losses are mitigated and the net loss figure is only around 6 lakhs.
***
Analysis of further such cases will be done, in details, in the final report.
风险测量和管理-Risk Measurement and Management:
As the commodity market evolves in India, the volumes traded increase, and so does the volatility - investors and companies feel the requirement of a risk measurement technique that can be used to limit the risk exposure in commodities. In order to manage risk, first we need to understand “Risk”. Risk is exposure to uncertainty. But risk is a very subjective term. What seems a risky trade to a small investor may be a natural trade for a big corporate client. Also there are many aspects to risk – all of which either an individual is not aware of or may not even understand.
Suppose a CEO wants to know the risk exposure of his firm – the manager knows that there is a certain amount of interest rate risk, market risk, currency risk etc. He can start by listing the company’s positions - but this is helpful only if the CEO understands all the positions, the instruments and the risks inherent in them. As such expressing risk to another person becomes very difficult.
Hence, there should be some means to “quantify” risk – one which can be easily understood even by a non-expert.
In this regard “Value At Risk” (VaR) is an important tool. Its simplicity and effectiveness is the reason for its worldwide popularity. According to Glyn A. Holton (1997), around the globe, organizations are racing to implement the new technology.#p#分页标题#e#
Value at Risk (abbreviated as VaR) is one of the most popular tools used to estimate exposure to market risks, and it measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level. It was developed in 1993 in response to those famous financial disasters such as Barings’s fall.
VaR is provides a single number to the investors summarizing the total risk in a portfolio of assets “VaR answers the question: how much can I lose with x% probability over a pre-set horizon” (J.P. Morgan, RiskMetrics–Technical Document). Suppose that a portfolio manager has a daily VaR equal to $1 million at 1%. This means that there is only one chance in 100 that a daily loss bigger than $1 million occurs under normal market conditions. With VAR we take the subjective notion risk and describe it in an objective manner.
As with stop loss limits, non specialists intuitively understand the meaning of VAR limits. For e.g. if its stated that 1 day 90% VaR is Rs 1L on an investment in Gold, then the non specialists automatically understands that the investment can lose less than 1L Rs on an average of 9 out of 10 days.
The role of the VaR model is to objectively define a range within which a firm’s risk should lie – as such it can be used as a “risk measure”.
计算VaR的概率分布的改变商品价值;信心水平为X %-Calculation of VaR from the probability distribution of changes in the commodity value; confidence level is X%
In general, when N days is the time horizon and X% is the confidence level, VaR is the loss corresponding to the (100 — X) th percentile of the distribution of the change in the value of the portfolio over the next N days. For example, when N = 5 and X = 97, it is the third percentile of the distribution of changes in the value of the portfolio over the next five days. Figure above illustrates VaR for the situation where the change in the value of the portfolio is approximately normally distributed. VaR is an attractive measure because it is easy to understand. In essence, it asks the simple question "How bad can things get?" This is the question all senior managers want answered.
时间范围-The Time Horizon
In theory, VaR has two parameters. These are N, the time horizon measured in days, and X, the confidence interval. In practice, analysts almost invariably set N = 1 in the first instance. This is because there is not enough data to estimate directly the behavior of market variables over periods of time longer than one day. The usual assumption is N-day VaR = 1-day VaR x (N)^0.5. This formula is exactly true when the changes in the value of the commodity on successive days have independent identical normal distributions with mean zero. In other cases it is an approximation.
VaR的主要用途-Main Uses of VaR:
VaR is not just a number that measures the risk of the firm. VaR can be used to manage risk pro-actively, and firms are finding ways to incorporate VaR to make decisions ranging from capital allocation to setting VaR based trading limits. Some of the uses of VaR are:
Hedge Timing : VaR can be used to analyse the hedge timing issue. Suppose that a producer, at a give time, recognizes the possible need of a futures contract for risk reduction purpose. Should the producer trade in the futures market immediately or should he wait? Conditions are characterized under which delaying the hedge decision is preferred as it produces a smaller VaR. For an efficient futures market, it appears that the producer is better off delaying the hedge decision as long as possible. However, strong backwardation promotes early hedging.
Analysis of Risk : A firm will always have natural hedges, but in most cases, it is necessary to manage risk proactively by taking a position in a derivatives contract, insurance, etc, due to the fact that not entering into such contracts will increase the overall risk of the firm. One might get into a particular derivative contract to offsetting a particular risk exposure of the firm. But this might end up increasing the overall risk exposure of the firm. Therefore, it is important to have systems and procedures in place to carry out a comprehensive analysis of risk for the firm instead of a trade-by-trade basis. VaR can do such comprehensive risk analysis.
As a measure of hedging effectiveness
Set up VaR-based trading limits, including real-time limits.
Create Risk Management reports for senior management to provide a better understanding of the firm’s overall risk profile.
如何计算VaR?-How to Calculate VaR?
There are multiple methods to calculate VaR:
历史模拟方法-Historical Simulation approach
模型建立方法-Model-building approach
蒙特卡罗方法-Monte Carlo method
二次模型-Quadratic model
All the above methods have some advantages or disadvantages. Historical Simulation is one of the most widely to measure VaR . It involves using past data in a very direct way as a guide to what might happen in the future. Suppose that we wish to calculate VaR for a commodity using a one-day time horizon, a 95% confidence level, and 300 days of data.
程序计算VaR的方法如下-Procedure to calculate VaR in this method is as below:
Last Traded futures exchange Price data on the particular commodity is collected from Bloomberg for the last 300 traded days.
The log returns on the commodity is calculated.
Scenario 1 is where the percentage changes is same as that of the first day. Scenario 2 is where the percentage changes is same as that of the second day and so on. This way we create 300 different scenarios.
These scenarios are then arranged in the ascending order of returns – i.e. the arranged from lowest to highest returns.
The first 15 worst daily changes in returns is the fifth percentile of the distribution.
The estimate of VaR is the loss when we are at this fifth percentile point. Assuming that the last 300 days are a good guide to what could happen during the next day; we are 95% certain that we will not take a loss greater than our VaR estimate.
实际的计算-Actual Calculations:
The calculations have been done using 300 data points till 30th March, 2010.
Data for VaR historical simulation calculation
日子-Day
日期-Date
LTP
日志返回-Log Returns
With these we have 300 different scenarios of changes in returns in the commodity. The value of the commodity on the last calculating day (30/3/2010 in the calculation shown) is known. Suppose this is Rs. 351.35. Using this value we can calculate the change in the value of the commodity between 30/3/2010 and 1/4/2010 for all the different 300 scenarios.
场景数量-Scenario Number
日志返回-Log Returns
The last column above shows 300 possibilities for the value of copper on 1/4/2010. These values are then ranked from smallest to largest. The first 15 (= 5% of 300 points) data points correspond to the worst 5% decrease in the value of copper. So the 15th Point 342.18 is the value we are looking for.
Hence it can be said that “We are 95% percent certain that the value of copper might decrease to 342.18 in the next 1 day." Hence a decrease of Rs 9.17 is the VaR.
Similar procedure can be used to calculate VaR of Copper for say 10 day :
N-day VaR = 1-day VaR x (N)^0.5
Therefore 10 day VaR of Copper is = 9.17 x (10)^0.5 = 28.99.
Hence “We are 95% percent certain that the value of copper might decrease to 322 (351.35 – 28.99) in the next 10 days."
The actual excel calculations of the above is attached in the annexure:
File name << VaR calculations for IR.xlsx >>
提前计划:(更多的将做什么)-Plan Ahead:
(What more will be done)
Literature review needs to be done to measure performance of Hedge Ratio using VaR.
Previous data would be collected and VaR would be implemented on all the commodities.
VAR would be used to effectively communicate the risk of a particular commodity, to the investor.
Hedge Timing: issue will be explored in details – As to how implementing VaR might help a hedger to further tune the hedging.
New Corporate Presentations would be designed clearly showing the benefits of hedging along with proving the performance of the hedging scenario using Value At Risk.
Will help Karvy in quantifying a risk limits for all its investments.
Lastly, as more details are known, it will be possible to pin point the limitations of the tools used in the study. The limitations will be explored in details.
Literature review would be done to find out if the other methods of calculating VaR is applicable for commodities. If yes, then VaR would be calculated using all the rest of the methods mentioned above.
Will interact with different firms to identify their commodity price risk exposures. We can then create tailored risk management products that mitigate these risks and protect operating margins.
CVaR would be implemented along with VaR. The CVaR measures the expected loss conditional on the loss being greater than or equal to the VaR.
Stress Testing would be done to check the validity of the model.
附录-Annexure:
Al Hedging.xlsx
Cu Hedging.xlsx
VaR calculations for IR.xlsx
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