Today we published a one hour movie that consolidates operationalrisk themes (except for Basel II, which is covered in a dedicated moviein two weeks). Today’s movie focuses on operational risk as reviewed byLinda Allen, Reto Gallati and Anthony Saunders (Core Readings IV. 4,IV. 6A, and IV. 10).
A key theme is that bottom-up approaches are better suited to identifying and distinguishing betweenhigh-frequency, low severity (HFLS) loss events and low frequency highseverity (LFHS) events. LFHS are "once in a lifetime," catastrophic events.Being, by definition, unexpected they are vexing to operational riskanalysis: a company cannot collect good data if they don’t oftenexperience catastrophes.
Gallatilargely describes the ideal future of operational risk. As a bank orcompany works through his stages, a bank is growing into an integrated, interdisciplinary, proactive organization that utilizes data-based insights to monitor and predict operational risk.
Remember the three essential elements of an ORM framework: standards, reporting infrastructure and a measurement methodology:
Finally,both Allen and Gallati tend to categorize approaches along twodimensions: top-down/bottom-up and quantitative/qualitative:
Posted in Operational Risk, Movie Releases | No Comments »
New Movie: Model & Liquidity RiskPosted by David Harper on 24th August 2006
Thisweek’s 30-minute movie reviews model & liquidity risk (readingsIV.1, IV.3 & IV.7). Holding sufficient cash on the balance sheet isthe CFO’s concern and that’s funding liquidity risk. Worrying about asset value deterioration is market liquidity risk:
Thefactors that impact an asset’s liquidation cost include time horizon,asset type, fungibility (i.e., how easy can we sell or replace theasset?), market microstructure and the bid-ask spread:
KevinDowd divides models into three types: fundamental, descriptive, andstatistical. A key theme in the Dowd reading is epistemological: whenwe apply a model, we cannot escape making an assumption. We may choose between assumption trade-offs, but no model can escape reliance on some a priori, unverified assumption. The point is to at least be aware of your assumptions!
TheRebonato reading is short but non-trivial. Rebonato’s definition ofmodel risk: the risk of a significant difference between themark-to-market model value (i.e., what value does the model give us,currently?) and the traded price (what price is the market giving?):
A key theme is that bottom-up approaches are better suited to identifying and distinguishing betweenhigh-frequency, low severity (HFLS) loss events and low frequency highseverity (LFHS) events. LFHS are "once in a lifetime," catastrophic events.Being, by definition, unexpected they are vexing to operational riskanalysis: a company cannot collect good data if they don’t oftenexperience catastrophes.
Gallatilargely describes the ideal future of operational risk. As a bank orcompany works through his stages, a bank is growing into an integrated, interdisciplinary, proactive organization that utilizes data-based insights to monitor and predict operational risk.
Remember the three essential elements of an ORM framework: standards, reporting infrastructure and a measurement methodology:
Finally,both Allen and Gallati tend to categorize approaches along twodimensions: top-down/bottom-up and quantitative/qualitative:
Posted in Operational Risk, Movie Releases | No Comments »
New Movie: Model & Liquidity RiskPosted by David Harper on 24th August 2006
Thisweek’s 30-minute movie reviews model & liquidity risk (readingsIV.1, IV.3 & IV.7). Holding sufficient cash on the balance sheet isthe CFO’s concern and that’s funding liquidity risk. Worrying about asset value deterioration is market liquidity risk:
Thefactors that impact an asset’s liquidation cost include time horizon,asset type, fungibility (i.e., how easy can we sell or replace theasset?), market microstructure and the bid-ask spread:
KevinDowd divides models into three types: fundamental, descriptive, andstatistical. A key theme in the Dowd reading is epistemological: whenwe apply a model, we cannot escape making an assumption. We may choose between assumption trade-offs, but no model can escape reliance on some a priori, unverified assumption. The point is to at least be aware of your assumptions!
TheRebonato reading is short but non-trivial. Rebonato’s definition ofmodel risk: the risk of a significant difference between themark-to-market model value (i.e., what value does the model give us,currently?) and the traded price (what price is the market giving?):
