Podcast: Mats Kjaer on how trades affect the balance sheet
Bloomberg quant has developed a balance-sheet model for XVA pricing
In this Quantcast podcast, I talk to Mats Kjaer, head of quantitative XVA analytics at Bloomberg, about his latest work on capital valuation adjustment (KVA).
Kjaer is well known in the industry for having produced a number of highly cited papers on derivatives pricing and, in particular, on credit valuation adjustment (CVA) and funding valuation adjustment (FVA). One of these works led him and his co-author at the time, Christoph Burgard, to be named Risk’s quants of the year in 2015.
In the balance redux, Kjaer’s newly published paper, which will also appear in the November issue of Risk, presents a structural model developed on the dealer’s balance sheet.
The balance-sheet approach was largely inspired by a recent paper by Leif Andersen, Darrell Duffie and Yang Song titled Funding value adjustments, and it’s a clear departure from the semi-replication method Kjaer adopted in his most previous works. “I wanted to have something consistent for KVA to fit in with other XVA metrics, but I didn’t want to use semi-replication,” Kjaer explains.
The model, set up so that it considers equity financing, regulatory capital and hedging, was initially developed in a single-period setting and later extended to continuous time to make it applicable in practice. “I’m proud to say that the resulting work was released in production last month,” Kjaer reveals.
Investigating XVAs from a balance-sheet perspective also offered him some valuable insights. “I started off with the aim of looking at KVA,” says Kjaer, but he gained “more knowledge about the other valuation adjustments like FVA and CVA”.
The method proves to be a comprehensive pricing tool, and quantifies the profitability of a trade from the viewpoint of the firm and that of the shareholder.
Furthermore, it aids in the visualisation of how much more expensive an unmargined swap is compared with a margined one, and shows how the credit rating of a counterparty affects the cost of the deal, making lower-rated firms less attractive.
Index:
00:00 Intro
02:05 XVAs so far, a brief recap
05:52 What is In the balance, redux contributing
06:52 What are the results?
09:18 How banks can use the balance sheet approach to XVA
09:55 Impact of counterparty rating and margins on the valuation
11:20 Importance of continuous time models
12:20 Implementation
15:30 Future development of the model
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