CURRENT IM METHODOLOGIES: WHAT YOU NEED TO KNOW
As a follow-up to our previous initial margin discussion, “Navigating the Murky Waters of Initial Margin for OTC Derivatives” regarding how to navigate the complexities of non-centrally cleared OTC derivatives, Part II of our analysis will outline and break down some of the standard methodologies most popularly used today.
Watch the related webinar which provides a detailed analysis of initial margin and its impact on the derivative markets, “Primer on Initial Margin and its Impact on Derivatives Markets.”
In our analysis, we describe what we consider to be the four most widely used, industry-accepted IM based methodologies. These include: Historical VaR (industry standard), Variance Covariance VaR, Monte Carlo VaR and Scenario-based methods (industry standard). To begin our analysis, we will first outline the steps involved in these methodologies:
Historical VaR (industry standard)
The Historical VaR approach includes the following steps:
Variance Covariance VaR
The Variance Covariance VaR approach involves the following steps:
Monte Carlo VaR
The Monte Carlo VaR method would clearly be a more computationally intensive approach, involving the following steps:
Scenario Based Approach (industry standard)
This approach is most suitable for exchange traded products, and very popular today. It relies on the following steps:
Potential Issues and Best Practices
Given the various IM methodologies outlined above, how can today’s OTC derivative practitioners decide what is the best methodology to use and when? Equally important, what are some potential pitfalls to look out for when utilizing these methodologies, which could result in significantly different IM calculations?
Clearly, one potential issue involves differences used during the market data preparation step—given different choices of market data sources and different choices of market data cleansing methodologies. A second potential issue could occur during the time series derivation and adjustment steps. Depending on the choice of the methodology, risk factors driving the simulations will differ. For example, would we be observing the changes in the movements of direct quotes (e.g. 10 year swap rate) or derived factors—such as zero rates or PCA-based factors? Furthermore, construction of more complex risk factors such as curves, volatility surfaces or cubes and associated interpolation/extrapolation techniques could introduce further complications.
In addition, when it comes to scenario generation and generation of portfolio price distribution for each scenario, some methodologies could result in different scenarios being generated (all else being equal) due to the choice of simulation models, random number generation and variance reduction techniques. Moreover, portfolio price distribution could differ for more complex derivatives, depending on the choice of pricing model.
Due to the potential issues described above, very different IM calculations can result. However, regardless of the choice of methodology,we can observe similar patterns across them— and that is why we recommend, as a best practice, keeping the following steps together:
Step 1: Market data preparation, including:
Step 2: Time series derivation, risk factor adjustments and scenario generation, including:
Step 3: Generation of portfolio price distribution for each scenario
Step 4: Calculation of Initial Margin
Conclusion: Managing the Ambiguity Surrounding Initial Margin Rules
Given the methodologies we’ve touched upon here, and the issues associated with them, we could envision the following potential developments taking place in the future to help alleviate collateral disputes around IM:
While the new regulations for OTC derivatives are an important step forward towards making our financial system a safer place, at this point there is still a lot of ambiguity around implementation of these rules and their cumulative impact.
For now, we believe institutions should be looking to minimize the costs of doing business by developing robust systems to help them measure the amount of initial margin and variation margin to be posted on the daily basis. We also recommend forecasting these measures into the future to help manage liquidity needs and to optimize collateral usage.