Podcast: Colin Turfus on short-rate models and Libor’s end
Deutsche Bank quant proposes a lean model to quickly produce benchmark prices
Colin Turfus, a senior quant analyst at Deutsche Bank in London and author of Risky caplet pricing with backward-looking rates, is our guest for this episode of Quantcast.
Turfus has spent the past six to seven years working on analytic solutions for short-rate models, tracking the evolution of interest rates over time. This stream of research has progressively extended the classic Hull-White model for short rates to incorporate the risk-free rate, which is essential for the transition from interbank offered rates (Ibors). His latest paper builds on research he has published in the past. “This particular paper is the extension of these formulae to be able to handle, in addition, credit risk, where credit is stochastically variant and there is potentially wrong-way risk associated with that,” Turfus explains.
The Hull-White model is a short-rate model for pricing interest rates that may be stochastic. It has proven popular because it provides an analytic solution for the zero-coupon model and therefore facilitates the pricing of options and swaptions based on Libor. As he points out in this podcast, while this is highly desirable, it may come at a price of a less adaptable setting, which is harder to manage when one needs to price different products.
Turfus is a model validation quant, and these models were developed with that purpose in mind, as the availability of an analytic solution allows for the quick creation of benchmark prices. But he sees wider applications.
“The most interesting application I see in the longer term would probably be more in market risk, where even though you have fairly simple derivatives, you often have to value them in the context of a portfolio where there are multiple other underlyings,” says Turfus.
His work in this area is not done yet. The next step will be overcoming the limitation of having a time-dependent deterministic volatility, in order to be able to fit the volatility smile. Early tests show this direction of research gives good results and frees the way to a more complete version of Turfus’s model.
Index
00:00 Intro
01:25 Analytic solutions for short-rate models
03:15 The Hull-White model
04:17 The Mercurio-Lyashenko model
05:35 The Black-Karasinski model
06:36 Practical applications
12:12 Closed-form models vs Monte-Carlo approaches
14:48 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
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
Podcast: Lorenzo Ravagli on why the skew is for the many
JP Morgan quant proposes a unified framework for trading the volatility skew premium