Podcast: Claudio Albanese on how bad models survive
Darwin’s theory of natural selection could help quants detect flawed models and strategies
At almost every financial firm, there will be some models that are unfit for purpose. But how do flawed models manage to exist for so long?
Claudio Albanese, founder and head of development at Global Valuation – and our guest for this episode of Quantcast – thinks Darwin’s theory of evolution may offer some insight into that question.
For his latest study, Albanese, together with Stéphane Crépey at Université de Paris and Stefano Iabichino of JP Morgan, adapted Darwin’s principles to show how low-quality models that overvalue and overhedge structured products can survive – and even thrive – in the short term.
Because traders can sometimes profit by overhedging certain derivatives structures, they may be incentivised to overlook the weaknesses of particular models. The trade-off is that they will end up taking long-term risks that are unaccounted for by the models, potentially leading to painful losses.
As an example, Albanese and his co-authors show the effect of using low-quality models to price range accruals – an exotic structured product with a fraught history. But their findings are relevant to other derivatives too – in particular, those with embedded callability.
Albanese argues a Darwinian lens may help banks detect bad models and inefficient hedging strategies – and avoid the dangerous exposures they create.
This foray into model risk is a departure for Albanese, who is known for his contributions to the study of derivatives valuation adjustments. On that topic, he shares his thoughts on the concept of a hedging valuation adjustment, which was recently introduced by Ben Burnett at Barclays and calls for it to be expanded beyond transaction costs.
That might be a subject for another podcast.
Index
00:00 Intro
02:50 Motivation for the model risk study
05:25 Range accruals case study
09:30 The risk of overhedging
13:10 Visualising model risk as it materialises
17:15 The connection between natural selection and derivatives models
21:15 What products are potentially affected by flawed pricing models?
25:00 Future research
To hear the full interview, listen in the player above, or download. Future podcasts in our Quantcast series will be uploaded to Risk.net. You can also visit the main page here to access all tracks, or go to the iTunes store or Google Podcasts to listen and subscribe.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Cutting Edge
Choosing trading strategies using importance sampling
The sampling technique is more efficient than A-B testing at comparing decision rules
A comparison of FX fixing methodologies
FX fixing outcomes are mostly driven by length of calculation window
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Backtesting correlated quantities
A technique to decorrelate samples and reach higher discriminatory power is presented
A hard exit threshold strategy for market-makers
A closed-form solution to derive optimal stop-loss and profit-taking levels is presented
Pricing share buy-backs: an alternative to optimal control
A new method applies optimised heuristic strategies to maximise share buy-back contracts’ value
CVA sensitivities, hedging and risk
A probabilistic machine learning approach to CVA calculations is proposed
Podcast: Alvaro Cartea on collusion within trading algos
Oxford-Man Institute director worries ML-based trading could have anti-competitive effects