Quant Finance Seminar

organised by

LMFG and Goldman Sachs

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Currently on hold. Previous events can be found below. 

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SPRING TERM 2020-2021

Date: Thursday 24 June 2021 

Registration: https://goldmansachs.zoom.us/webinar/register/WN_YMx9y82oTTSZSwUwrWDN3A

Speaker 1: Mathieu Rosenbaum

Time: 16:00-16:45

Title: A rough volatility tour from market microstructure to VIX options via Heston and Zumbach

Abstract: In this talk, we present an overview of recent results related to the rough volatility paradigm. We consider both statistical and option pricing issues in this framework. We notably connect the behaviour of high frequency prices to that of implied volatility surfaces, even for complex products such as the VIX.

Speaker 2: Iacopo Mastromatteo

Time: 16:45-17:30

Title:  Price impact kernels from adversarial games : the Stationary Kyle model

Abstract: We propose a model of market microstructure that is both i) empirically grounded on stylized facts of market microstructure (long-range correlation of order flow, diffusive nature of prices) and ii) economically micro-founded (emerges as an equilibrium of a game between rational agents). This framework predicts a universal strongly persistent (semi-permanent) impact function at high frequency, and a non-universal transient impact function at lower frequencies. Analytical examples in a solvable Markovian framework will be provided, along with numerical results to get insights on more realistic cases.

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Date: Thursday 18 March 2021 

Registration: https://goldmansachs.zoom.us/webinar/register/WN_TrKy3_kMQbG4Qm4GnE_Vcw

Speaker 1 : Alexei Kondratyev 

Time: 17:00-17:45

Title: Quantum Neural Networks

Abstract:  The main focus of applied quantum computing research is an experimental demonstration of the quantum advantage. It is highly likely that the emerging discipline of Quantum Machine Learning (QML) will be the first to produce a definite evidence of quantum advantage using a hybrid quantum-classical approach to training and running the Quantum Neural Networks. With exceptionally fast rate of quantum hardware development we can detect the first signs of the quantum advantage on finance-related use cases.

Speaker 2: Stefan Woerner

Time: 17:45-18:30

Title:  Towards Quantum Advantage in the Financial Service Sector

Abstract: Quantum computing promises speed-ups for several applications relevant in the financial service sector.

In this talk, we will discuss some quantum algorithms to speed up Monte Carlo simulation as well as quantum heuristics for combinatorial optimization and machine learning.

Further, applications such as option pricing, credit risk analysis, and portfolio management will be analyzed with respect to their potential quantum advantage and requirements to achieve it.

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Date: Wednesday 24 February 2021 

Registration: https://goldmansachs.zoom.us/webinar/register/WN_HxjotaFARfq58E843SKCHQ

Speaker 1:  Alexander Lipton 

Time: 17:00-17:45

Title: Financial supernova: Emerging Trends in Decentralized Finance (DeFi)

Abstract: In this talk, we discuss some of the most recent developments in the cryptocurrency ecosystem. Specifically, we review stable coins, their classification, potential applications, and related topics, and closely related emerging field of DeFi (Decentralized Finance), including Automated Market Makers (AMM), yield farming, and other peculiar concepts. We explain mathematics, economics, and technology behind these developments and elaborate on their pros and cons.

Speaker 2:  Maxime Boonen

Time: 17:45-18:30

Title:  An inside look at crypto market structure

Abstract: In this talk, Maxime will present the structure of the crypto market through three different lenses: the participants, the technology and the products. He will expound on the interaction between institutional and retail players, the role of banks and what lessons the old guard and the new guard can teach one another.

AUTUMN TERM 2020-2021

Date: Wednesday 9 December 2020 

Registration:  tbc

Speaker 1: Fabio Mercurio (Bloomberg)

Time: 17:00-17:45

Registration: online

Title: Looking Forward to Backward-Looking Rates: A Modeling Framework for Term Rates Replacing LIBOR

Abstract:   LIBOR and other similar IBOR rates represent the cost of short-term funding among large global banks, and are the reference rates in millions of financial contracts with a total market exposure worldwide of hundreds of trillion dollars. Lack of liquidity in the unsecured short-term lending market, as well as evidence of LIBOR manipulation during the 2007-09 credit crisis, led regulators to identify new rate benchmarks. In this talk, we introduce and model the new new interest-rate benchmarks and their compounded setting-in-arrears term rates, which will be replacing IBORs globally. We show that the classic interest-rate modeling frameworks can be naturally extended to describe the evolution of both the forward-looking (IBOR-like) and backward-looking (setting-in-arrears) term rates using the same stochastic process. We then introduce an extension of the LIBOR Market Model to backward-looking rates. Applications will be presented and numerical examples showcased.

Speaker 2:   Vladimir Piterbarg (Natwest Markets)

Time: 17:45-18:30

Title: Libor reform and the arc-sine law

Abstract:

• Fallback Libor adjustment spread will be calculated as a median of a time series of Libor-RFR spreads as observed on the Libor cessation announcement date
• Median is a non-linear function of future observations of Libor-RFR spread
• Its expected value depends on volatilities of future spreads, as well as observed history so far
• We propose a model and build a numerically efficient approximation to the expected value of a median based on Arc-Sine Law and study the impact of future dynamics of the Libor-RFR spreads on the expected value of the Libor adjustment spread

Date:

Tuesday 29 September 2020 

Speaker: Lukasz Szpruch (University of Edinburgh)

Time: 16:00-18:00

Registration:   online  by Friday 25 September; please register as 'client' giving Camille Humbert as your 'contact' 

Title: Robust pricing and hedging via neural SDEs

Abstract: Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are "black-boxes" as individual parameters do not have a meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data. The resulting model called neural SDE is an instantiation of generative models and is closely linked with the theory of causal optimal transport. Neural SDEs allow consistent calibration under both the risk-neutral and the real-world measures. Thus the model can be used to simulate market scenarios needed for assessing risk profiles and hedging strategies. We develop and analyse novel algorithms needed for efficient use of neural SDEs. We validate our approach with numerical experiments using both local and stochastic volatility models. We will also show that neural SDEs can be used to calibrate to SPX/VIX options.

The talk is based on joint work with: P. Gierjatowicz, A Jacquier, M. Sabate-Vidales, D. Siska and Z Zuric.