![Risk.net](https://www.risk.net/sites/default/files/styles/print_logo/public/2018-09/print-logo.png?itok=1TpHrpuP)
Podcast: Hans Buehler on deep hedging and the advantages of data-driven approaches
Quant says a new machine learning technique could change the way banks hedge derivatives
![Hans Buehler Hans Buehler](/sites/default/files/styles/landscape_750_463/public/2019-05/Hans-Buehler.jpg.webp?h=343f4de4&itok=eurm-pou)
Hans Buehler, global head of equities analytics, automation and optimisation at JP Morgan, visited our London offices to record a podcast on a recently published paper he co-authored on a new technique called deep hedging.
The quant argued this new machine learning technique can hedge derivatives without the need to use classical models such as Black-Scholes. Typically, banks use risk sensitivities known as Greeks derived from classical models to hedge their options books, but these methods are limited in their ability to factor in transaction costs and additional market information. With deep hedging, machines can learn from large amounts of historical data to make more precise hedging decisions, said Buehler.
“Every trader who uses all these classical models will tell you there are some overrides, [such as] delta skew or barrier shifts… none of these are systematic usually in the strict mathematical sense, so it keeps requiring human input to maintain those [overrides],” said Buehler. “Then the machine learning techniques came up which made it possible to do much heavier, much more precise calculations.”
Buehler argued the approach also aligns with the way traders actually think about hedging, as the objective is mainly to reduce hedging error, or the difference between the hedged item and the hedge.
“It fundamentally does what people actually do when they trade. In reality, they [ask], ‘What do I need to do in order to minimise my hedging error in the sense of P&L uncertainty?’ rather than saying, ‘How much vega do I have?’. It is very data driven,” Buehler added.
Another advantage is that the technique allows for more automation of hedging, as machines can run in parallel to identify appropriate hedges. This can make the process faster.
“If I wanted to run a lot of books of options in parallel, it is very difficult for humans to observe the vega exposures on a lot of single stocks in parallel because each book is very specific,” said Buehler.
The technique is currently being applied to index options books, although this can be expanded to more liquid vanilla products, Buehler said. One caveat is that less liquid over-the-counter products may be hard to apply this technique to, as data is sparse for these instruments.
Index
0:00 Introduction
1:20 What is deep hedging?
4:32 Applications of deep hedging
6:12 Advantages of not using Greeks
8:08 Sparse datasets
9:10 Asset classes applicable
10:23 Operational changes from adoption of the technique
11:42 Benefits to exotics and illiquid products
12:47 Caveats to deep hedging
13:35 The black box problem
14:27 Will banks adopt this on a larger scale?
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.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net
More on Markets
Vanguard stages swaptions comeback
Counterparty Radar: Deutsche grew book to $11 billion in Q1 to become largest non-US swaptions dealer to mutual funds
Data reveals hidden clockwork of FX forwards market
More than 70% of Vanguard’s volumes and nearly half of Pimco’s regularly occur on just four calendar days
RJ O’Brien plots expansion into US Treasury clearing
Chairman and CEO says futures house plans to capitalise on SEC’s new mandate for interdealer trades
Premialab adopts CDM for QIS swap booking
Vendor selects open source data model to power expansion in growing systematic index market
Is JSCC-CFTC stalemate about to be broken?
Japan CCP gains allies in battle to clear yen swaps for US clients, but CFTC shakeup could dash hopes
What T+1 risk? Dealers shake off FX concerns
Predictions of increased settlement risk and later-in-the-day trading have yet to materialise
How steepener trades burned hedge funds, and what happened next
Delays to central bank rate cuts torpedo popular trade, causing funds to pull capital – to the chagrin of sell-side desks
Covid-induced Eurobonds mark step towards EU financial cohesion
Successful issuance points to greater pan-European sharing of risk
Most read
- Harvesting the FX skew premium
- How steepener trades burned hedge funds, and what happened next
- House of cards? The $3 trillion (non-systemic) real estate risk