A risk manager is tasked with measuring so many risks. Their mandate is broad and wide reaching: from measuring the size of the book to determining the right collateral is posted. The primary goals of Risk Management will help us drill down to the more granular risks to pay attention to:
Work closely with your analysts as well as sales people so you have all information on every trading desk, relationship manager (loans), client or prospect (asset purchases or mgmt.) and able to make final decisions.
This overview provides a very shallow overview of implementing and operating a Market Risk management group.
Risk Management is, at last, getting our industry’s full attention. I hope that we can move forward and engage in constructive dialogue with our regulators and remain compliant. Risk Management encompasses several areas of the derivatives value chain. We’ll cover each area in turn.
Market Risk is calculated using Value-At-Rick or VAR. VAR calculates a probability statement such as: “I’m x% confident we won’t lose more than V dollars over the next N days”. It allows institutions to have a single number to define their market risk given the parameters used. Regulators require banks to report a 99% confidence level over the next 10 days. BIS requires the banks to have three times their 10 day VaR in capital.
Bank Trading desks use the 99% & 95% VAR number for 1 day (tomorrow).
Hedge Funds use a one day 95% confidence figure.
So bank traders want to know how the other side of their trade might respond if the market moves big the next day. Although most money center banks now use Monte Carlo simulation not normal distribution. But using volatility (the square root of variance which presume the % returns are normally distributed) is easier to conceptualize.
There are three accepted methods of calculating VaR: For sake of brevity we will only discuss variance-co-variance in detail.
Historical simulation uses the ACTUAL returns of the assets in which the bank has positions. They typically use the previous 200–500 days of actual price changes. EXAMPLE: using the last 200 days and calculating the daily returns they give you the actual 180th worst loss (given the Bank’s current position). Whatever loss is the 180th worst loss, while be reports as their VaR. The 180th worst loss out of 200 changes gives you the 90% confidence, one day VaR (180/200 = 90%)
The easiest way to view Monte Carlo Simulation begins with thinking of it as similar to Historical Simulation. EXCEPT, Historical Simulation implies the actual 180th worst loss in the past 200 days can re-occur on the exact same date over the next time frame (10 day or 1 day). Monte Carlo uses a random number generator and uses 10,000 “trials” (you can view this as daily changes) to estimate the price return of the asset at the end of the analysis horizon (10 or 1 day). However, you will find the detailed process for calculating VAR using Monte Carlo Simulation here.
The variance-co-variance method uses the historical volatility of the asset over the past X number of days (typically 200 – 500 days).
Perhaps you recall the bell-shaped curve. If not, let’s run through it quickly now: Normal distribution presumes the mean of the data series is zero. The resulting volatility number you hear in the market (i.e. , VIX) is the annual volatility. We can use the annual volatility to make probability statements which are described as standard deviations from the mean:
If you look at the N= file attached here, you’ll see a 99% confidence interval on a normally distributed as is ~ 2.33 standard deviations.
We can adjust the annual volatility to a shorter time frame by adjusting the annual figure by the square root of time. Let’s calculate the daily VaR, using the following example:
|EXAMPLE:||long a single asset valued at 100 million.|
|<200 day historical volatility is 25%.|
|For 10-day volatility we need to adjust our annual number by the SQRT of 365/10 as there are 365 CALENDAR days in a year. PLEASE NOTE: WE ARE NOW USING CALENDAR DAYS.|
|10-day Volatility = SQRT () = 6.0415|
|10 day Vol = .25 / 6.0415=4.303% PER 10 calendar day period.|
|We now need to adjust our daily volatility figure to reflect 2.33 stdev’s|
|4.303% * 2.33 = 9.97%|
|$100 million * .0997 = 10- day 99% var of $9,997,000|
|“I’m 99% confident we won’t lose more than $9.997 million on this position over the next 10 calendar days. ”|
It may be obvious that VaR focuses on the likely losses, but says little about the black swans. For a while we performed back testing and stress resting to ensure our VaR variables were correct. But notice, even if the volatility estimate was correct, the furthest I could go is “I’m 1% confident I’ll lose more than VaR tomorrow”. It could not tell me the size of that tail risk. For this reason, VaR has been challenged and looking to be replaced by Potential Exposure. This and other Trading Desk related risk measurements will come under the umbrella of Fundamental Review of the Trading Book (FRTB).
IF YOU’D LIKE A FREE RANGE CALCULATOR WHICH WILL CALCULATE THE PROBABILITY FOR A GIVEN VOLATILITY, TIME AND STARTING ASSET PRICE – REGISTER FOR THE SITE. YOU’LL RECEIVE THE RANGE CALCULATOR NOW AND A LOT MORE GOODIES OVER TIME.
In simple terms, counterparty credit risk is the risk of a customer (counterparty) defaulting on payment. There are levels of default, some easier to cure than others. A technical default is more easily cured than a straight “failure to pay” simply because the counterparty can prove receipt of said funds in a short time period. The delay in payment could be the result of human error, a failure to receive funds from their counterparty, etc. A “failure to pay” is a straight default where the counterparty can provide no assurance as to when they will be able to pay.
For uncleared swaps, counterparties will operate as they’ve done since the 1980’s. The counterparties had risk to each other — with the counterparty “in-the-money” having the risk that his counterparty be unable to meet their obligations. The mitigation of this risk will be managed in a similar manner, using similar documentation. You can read the details of uncleared documentation here. [ NEED URL/LINK ]
The counterparty risk of cleared derivatives more fully mitigates counterparty credit risk, especially from the client’s point of view. For the individual clearing swaps, they are insulated against contagion of other clearing clients defaulting. For the clearinghouse as long as they’re fully operational, have a robust Risk Management Committee and meet with other CCP’s regularly they are able to provide this risk mitigation. But because of the number of moving parts (many dealers and many clients), the probability associated with default is larger.
Credit risk is different from counterparty credit risk. Credit Risk relates to the default of assets in your portfolio.
When assigning a Credit Risk limit to a bond portfolio many risks are taken into account. Each risk is measured separately and as a portfolio of the same risk in other bonds.
Operational Risk speaks to any risk along the value chain. They are the errors which typically combine human error and misunderstanding.
The areas known as “operational” are any risk which can be ascribed to events which placed the institution at risk of fine(s) or censure.
Banks, Broker-Dealers, Insurance Companies and other financial services related entities are examined by their Regulatory Oversight Organizations. Regulatory Examinations are vital to an organization. Personnel is given strict orders not to answer any questions and is given the name and contact information to whom they should refer the questioner. Eye contact was limited and the examiners were given their own space from which to work. The examiners were not allowed in the trading rooms or on floors where senior management would greet and meet with clients.
If certain notices to “cure” are given by regulators, the letter includes a period during which they were required to cure the issue. For example, if credit work had been delayed and was listed by regulators to review the entity and provide the updated report to the examining regulator. The cure list could include anything from credit reports to the number of time stamps on an execution ticket.
Other types of risk are pure human error. For example:
Integrated Training, as taught by MHDS is an online or blended program given to the entire value chain. We pay particular attention to those job functions that worked directly before or after a specific function. For example, each middle office job function role play so that affirmation understands the process of submitting the trade (pre-affirmation) and ensuring receipt of a signed confirmation (post-confirmation). This allows all three functions to introduce redundancies and work as a well oiled team.
The ISDA Master Agreement is an overarching contract covering all derivatives contracts with a customer. Even from the first iteration in 1992, most of the terms were standardized. Today in a post Dodd-Frank era The ISDA Master Agreement for Uncleared Derivatives is even more standardized that before.
On that basis a Specified Entity is an affiliate of a counterparty which is covered by the Master Agreement for Cross Default in the event of bankruptcy or default of a subsidiary. The fifth Termination Event is called Credit Event Upon Merger (Section 5(b)(v) in 2002 ISDA Master Agreement). The aim is to draw in those members of your counterparty’s group (such as its parent or asset rich fellow subsidiaries) whose relationship is so close to your counterparty that if an Event of Default happened to them it would be very likely to affect your counterparty seriously too. A specified entity is one that is crucially important to the Parent Company or financially substantial companies to a Holding Company:
In all cases which Specified Entities apply to each party is a credit decision. Specified Entities is a way of collapsing down all transaction at once. This can provide an advantage to the customer who may have profitable swaps on one entity and losing swaps in another. Cross default may mean the counterparty doesn’t have to pay a loss it cannot afford. Big companies are often able to negotiate that Specified Entities will not apply.
While Specified Entities are not always needed, a counterparty which is a corporate holding company whose main financial substance of the corporate’s holding company is in those subsidiaries, the bank’s credit officer may want them as Specified Entities.
The Addendum A includes very specific terms and conditions.
Another document which has become more standardized is the SCSA, which spells out the threshold level when collateral will be required. Today this includes:
The SIMM codified uncleared derivative contracts and made them all more standard so as to place all counterparties on the same level.