Whitepaper on P&L Attribution now available for download

Posted by on Aug 22, 2012 in Classification, Download, P&L, P&L Attribution, Risk, Whitepaper and Downloads | 0 comments

Our whitepaper on P&L attribution (PLA) is now available for download.

The paper examines the practice of PLA production, analysis and reporting within banks. Given the recent regulatory focus  on PLA and banks’ capacity to produce it, the report also examines areas of potential interest i.e. policy, governance, process capacity and metrics that can be used to benchmark the bank’s capacity to produce, analyze, monitor and report PLA.

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P&L Attribution – Judging the weathermen

Posted by on Aug 22, 2012 in Classification, Modeling, Noteworthy, P&L, P&L Attribution, Regulatory, Risk | 1 comment

“The storm starts, when the drops start dropping
When the drops stop dropping then the storm starts stopping.”
― Dr. Seuss, Oh Say Can You Say?

“Pray don’t talk to me about the weather, Mr. Worthing. Whenever people talk to me about the weather, I always feel quite certain that they mean something else. And that makes me so nervous.”
– Oscar Wilde, The Importance of Being Earnest, Act 1

We will talk about weathermen and the predictions they make. And we will mean something entirely different. By weathermen, we will mean the models in a bank and the predictions they make or the hypotheses they form. And for the realism of Dr. Seuss’ drops dropping, we will substitute the realism of P&L..  More specifically, we will talk about P&L attribution (PLA) and the role it plays in helping us use the realism of P&L to test the hypotheses posed by our various risk models – which actually is its primary purpose in life.

We will focus specifically on 3 hypotheses formulated by a bank’s risk models, its VAR model and its CVA/EPE model respectively. Namely, for a given bank:

I.         Change in the mark-to-market value of its positions are materially determined by changes to a specified set of variables and parameters (i.e. risk factors) and the expected change is quantified by the sensitivities obtained to these risk factors from its models;

II.         There is a specified % probability that the value of its positions will lose more than its VAR number over any given interval equal to the VAR holding period;

III.         The cost of insuring its aggregate positions against the risk of counterparty Z defaulting is not expected to exceed the cumulative sum of the CVA fees charged to its trading desks for originating exposure to counterparty Z.

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