2 citations · 4 across the 4 of their papers we have counts for
6 papers
Deep Partial Hedging
Songyan Hou, Thomas Krabichler, Marcus Wunsch
Using techniques from deep learning (cf. [Büh+19]), we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in t…
Hedging Goals
Thomas Krabichler, Marcus Wunsch
Goal-based investing is concerned with reaching a monetary investment goal by a given finite deadline, which differs from mean-variance optimization in modern portfolio theory. In…
A deep learning model for gas storage optimization
Nicolas Curin, Michael Kettler, Xi Kleisinger-Yu +4
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize tec…
Deep Replication of a Runoff Portfolio
Thomas Krabichler, Josef Teichmann
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key n…
A constraint-based notion of illiquidity
Thomas Krabichler, Josef Teichmann
This article introduces a new mathematical concept of illiquidity that goes hand in hand with credit risk. The concept is not volume- but constraint-based, i.e., certain assets can…
The Jarrow & Turnbull setting revisited
Thomas Krabichler, Josef Teichmann
We consider a financial market with zero-coupon bonds that are exposed to credit and liquidity risk. We revisit the famous Jarrow & Turnbull setting in order to account for these t…