Quant Invest Summit - finance.knect365.com Invest Summit Inspiring next gen quant investment strategies ... It provides detailed examples of trading and risk management of popular

14-18 May 2018 Lisbon Marriott Hotel, Lisbon

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Quant Invest Summit Inspiring next gen quant investment strategies

Monday 14 May 2018

Broadway Room 07.40 Registration & welcome coffee

08.20 Chairman’s opening remarks

Market conditions: Interest rates volatility and liquidity

08.30

Non-fundamental market sell-off and “volatility feedback loops”: A market impact perspective How to change the design of products and your hedging strategy using derivatives to better manage interest rate risk and ALM? Aymeric Kalife, Head of Savings & Variable Annuities & Deputy Group Life Chief Actuary, AXA GROUP

09.10 Unified liquidity risk management framework: A big data / machine learning approach Stefano Pasquali, Managing Director, Head of Liquidity Research, BLACKROCK

09.50 Using machine learning methods for volatility trading Examining Models for realized volatility estimation and forecast. Applying machine learning to model selection, volatility trading and asset allocation Artur Sepp, Director & Senior Quant, JULIUS BAER GROUP

10.30 Morning coffee & networking break

How are quants cornering the alternative beta market?

11.00 Panel discussion Passive aggressive: Will smart beta strategies eclipse active management? Weighing up the performance of passive/smart beta strategies alongside active managers and hedge

funds

Philip Stoltzfus, CEO, THAYER BROOK PARTNERS LLP

Simon Weinberger, Managing Director, Scientific Active Equities, BLACKROCK

Michael Steliaros, Global Head of Quantitative Execution Services, GOLDMAN SACHS

11.40 Alternative Beta and quant ETFs: How accurately can they capture hedge fund performance? How significant are the varying alternative beta factors to determine a more robust expectation of performance? Alexandru Agachi, Co-Founder & COO, EMPIRIC CAPITAL

12.20 Latest advances in systematic investing Nick Baltas, Head of R&D, Systematic Trading Strategies, GOLDMAN SACHS

13.00 Lunch

Asset allocation and portfolio management

14.00 The evolution of asset allocation A journey from the first quantitative techniques to the age of AI Charbel Gereige, Software Developer, BLACKROCK

14.40 Factor portfolio construction and factor timing recent developments on factor portfolio construction touching on insights on bottom up vs. top down approaches Daniel Giamouridis, Global Head of Scientific Implementation Global Portfolio Products, BANK OF AMERICA MERRILL LYNCH

15.20 Afternoon tea & networking break

Generating alpha through quant innovations

15.50 Machine learning in asset management Why machine learning, why now and for who? Grigorios Papamanousakis, Deputy Head of Systematic Asset Solutions, STANDARD LIFE ABERDEEN


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16.30 Alpha generation – Staying ahead of the crowd

Views from a boutique short-term quantitative manager

Nicolas Mirjolet, Founding Partner and Chief Investment Officer, TOLOMEO CAPITAL

17.10 Deep Trading

Applications of deep reinforcement learning to systematic trading

Richard Turner, Director of Research, THE CAMBRIDGE STRATEGY

17.50 Chairman’s closing remarks

18.00 Networking drinks reception & champagne roundtable discussion groups - G1 A chance for everyone to network and relax after the day’s presentations and discussions. Champagne roundtables offer delegates a chance to delve deeper into timely topics of the day and

network with specific VIP speakers (sign up required)

Roundtable 1: Is alpha unlimited? Led by Alexandru Agachi, Co-Founder & COO, EMPIRIC

CAPITAL

Roundtable 2: Deep Learning: Opportunities and limitations for systematic investment Led by Ian

McWilliam, Analyst, ABERDEEN STANDARD INVESTMENTS

Roundtable 3: Q-learning based algos: how well do they work in practice? Led by Matthew Dixon,

Assistant Professor of Finance, ILLINOIS INSTITUTE OF TECHNOLOGY

Roundtable 4: Quantum Computing beyond the hype Led by Davide Venturelli, Quantum

Computing Lead, NASA-USRA QUANTUM AI LAB

19.30 End of Quant Invest Summit


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Quant Tech Summit Latest developments & practical implementations of blockchain, big data,

machine learning & HPC Monday 14 May 2018

Manhattan Room 07.40 Registration & Welcome Coffee


How is blockchain and cryptocurrency changing business in the financial world?

08.30 The future of wealth management using fintech and blockchain integration How crypto assets will change the investments landscape Daniele Bernardi, CEO, DIAMAN SCF

09.10 Crypto-currencies – An emerging asset class Explaining in detail blockchain technology and crypto-currencies as an investment vehicle, realities and challenges of setting up a crypto asset manager and the strategies used for alpha generation Anton Golub, Co-founder and Chief Science Officer, LYKKE CORP

09.50 Examining real possibilities and applications of Distributed Ledger Technologies Which applications are game changing for finance and why? Massimo Morini, Head of Interest Rate and Credit Models, BANCA IMI


Breakthroughs in big data and machine learning that are transforming the way quants operate

11.00 Building practical data visualisation applications How to draw conclusions from your numerical data using visual tools

11.40 Panel discussion Machine Learning in practice: Robustness and controls Strategies to effectively navigate the trade-off of predictive-performance and robustness and the kind of controls you need during training and prediction to implement them Moderator: Suhail Shergill, Director Data Science and Model Innovation, SCOTIABANK Matthew Dixon, Assistant Professor of Finance, ILLINOIS INSTITUTE OF TECHNOLOGY Ian McWilliam, Analyst, ABERDEEN STANDARD INVESTMENTS

12.20 Recent advances in probabilistic time series forecasting Discussing High Performance Computing aspects of training and parallel time-series predictions Leo Razoumov, Principal Machine Learning Scientist, AMAZON

13.00 Lunch

Machine learning: Finding the right use-cases

14.00 Reinforcement learning in finance Sharing recent developments in reinforcement learning, portfolio construction and trading Erdem Ultanir, Quantitative Credit Risk Analytics Lead, BARCLAYS

14.40 Predicting rare events with long short term machines Examining a novel oversampling technique for times series Matthew Dixon, Assistant Professor of Finance, ILLINOIS INSTITUTE OF TECHNOLOGY


HPC and quantum computing – What new problems can be solved?

15.50 Opportunities in the era of quantum technologies A gentle introduction to quantum computing with a focus on AI applications Alejandro Perdomo Ortiz, Quantum Machine Learning Lead, NASA-USRA Quantum AI Lab


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16.30 Practitioner perspective Industry breakthroughs in quantum computing An inside glimpse into how quantum computing is starting to be applied to finance Alexei Kondratyev, Managing Director, Financial Markets, STANDARD CHARTERED BANK Davide Venturelli, Quantum Computing Lead, NASA-USRA QUANTUM AI LAB

17.10 Google case study Examining the advantage to be gained from quantum optimisation How is the Google Quantum Artificial Intelligence Lab using new hardware and computer architecture to accelerate computations? Vasil Denchev, Chief Quantum Software Architect, Quantum Artificial Intelligence Lab, GOOGLE

17.50 Chairman’ closing remarks

18.00 Networking drinks reception & champagne roundtable discussion groups - G1 A chance for everyone to network and relax after the day’s presentations and discussions. Champagne roundtables offer delegates a chance to delve deeper into timely topics of the day and


Roundtable 1: Is alpha unlimited? Led by Alexandru Agachi, Co-Founder & COO, EMPIRIC

CAPITAL

Roundtable 2: Deep Learning: Opportunities and limitations for systematic investment Led by

Ian McWilliam, Analyst, ABERDEEN STANDARD INVESTMENTS

Roundtable 3: Q-learning based algos: how well do they work in practice? Led by Matthew

Dixon, Assistant Professor of Finance, ILLINOIS INSTITUTE OF TECHNOLOGY

Roundtable 4: Quantum Computing beyond the hype Led by Davide Venturelli, Quantum

Computing Lead, NASA-USRA QUANTUM AI LAB

19.30 End of Quant Tech Summit


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Volatility Workshop Monday 14 May 2018

This workshop covers many practical aspects of volatility data, modelling, risk management, and trading. It provides notably a step by step explanation of how to construct a volatility surface, how to implement a Local Volatility model and various extensions of it, how to price and manage variance swaps, how to exploit links between various volatility derivatives. It provides detailed examples of

trading and risk management of popular exotic products.

Berlin A Room Workshop leader: Bruno Dupire, Head Of Quantitative Research, BLOOMBERG L.P.

8.20 Registration, breakfast & networking time

9.00 Workshop leader’s opening remarks

9.05 Fundamentals

Historical volatility estimation and implied volatility calculation

How to construct a good implied volatility surface

How to compute a fair skew in the absence of options

Market facts: volatility regimes, handling earnings


11.00 Volatility models

Review of the most commonly used volatility models: Black-Scholes, Local Volatility model, Heston model, SABR models, stochastic local volatility model. Path dependent models, fractional volatility

Implementation of the Local Volatility model

Implementation of Local Stochastic Volatility models

Machine Learning to create data driven models

Case studies: Barrier options, AutoCallables and Accumulators

12.30 Lunch

13.30 Volatility derivatives

Variance swaps, replication, practical issues

Volatility swaps

Cross corridor variance swaps

VIX: Spot, Futures, options and ETFs

Options on realized variance

15.00 Afternoon coffee & networking break

15.30 Volatility trading & arbitrage

Volatility as an asset class

Frequency/phase arbitrage

Skew trades

Term structure of VIX arbitrage

Earning trades: 3 ways to play forward variance

17.00 Workshop leader's closing remarks

17.15 End of workshop


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Main Conference Day 1 Tuesday 15 May 2018

07.40 Registration & welcome coffee


New York

In the boardroom discussions

Geneva (Limited space: First-come first-served!)

08.30 Guest behavioural economic insight: Simple heuristics that make us smart

Weighing up real world heuristics vs. theory

Gerd Gigerenzer, Director, MAX PLANCK INSTITUTE FOR HUMAN

DEVELOPMENT AND HARDING CENTER FOR RISK LITERACY IN BERLIN

08.25 – 09.10 Chairman Introduction The future of LIBOR Fabio Mercurio, BLOOMBERG L.P.

09.10 Global regulations: Interpreting the latest quantitative implications for banks & buy-side How is today’s regulatory environment impacting the financial quantitative landscape? How to comply time and cost efficiently whilst maintaining strong profitability?

09.10 – 09.50 MiFID II impacts on trading Michael Steliaros, GOLDMAN SACHS

09.50 Cross-sector panel discussion

Frontiers in big data, machine learning and supercomputing What Finance can learn from scientific applications Moderator: Marcos López de Prado, Senior Lecturer, CORNELL UNIVERSITY David Leinweber, Founder, Center for Innovative Financial Technology, LAWRENCE BERKELEY NATIONAL LABORATORY Leo Razoumov, Principal Machine Learning Scientist, AMAZON Horst Simon, Deputy Director, LAWRENCE BERKELEY NATIONAL LABORATORY Alejandro Perdomo Ortiz, Quantum Machine Learning Lead, NASA-USRA Quantum AI Lab

09.50 – 10.30 Blockchains, with and without Bitcoins Helyette Geman, BIRKBECK - UNIVERSITY OF LONDON & JOHN HOPKINS UNIVERSITY

10.30 – 11.10 SACCR – The basics of how it is implemented


STREAM A

Algo Trading, E-Trading &

Machine Learning

Manhattan

STREAM B

Interest Rate

Modelling

Broadway

STREAM C

Option Pricing &

Volatility

Berlin

STREAM D

CCR, Collateral &

Central Clearing

Los Angeles

STREAM E

Behavioural Psychology

Geneva

11.40 Chairman’s opening

remarks

Pierce Crosby,

STOCKTWITS

Chairman’s opening

remarks


remarks


remarks

Masterclass in social intelligence: Intuition, cooperation and influencing people Gerd Gigerenzer, MAX PLANCK INSTITUTE FOR HUMAN DEVELOPMENT AND HARDING CENTER FOR RISK LITERACY IN BERLIN

11.45 Using AI for trade anomaly detection Alexander Giese, UNICREDIT

Negative rates,

negative fees

Luis Seco,

UNIVERSITY OF

TORONTO

Analytic formula

for barrier option

pricing

PETER AUSTING,

CITADEL

Counterparty loss

modelling for

CCAR

Matthias Arnsdorf,

JPMORGAN

CHASE

12.25 Bayesian asset

pricing for

algorithmic

trading

Vinayak Pathak,

SCOTIABANK

Approximate Local

Volatility Model for

Vanilla Rates

Options

Sebastian

Schlenkrich,

D-FINE GMBH

Theta I – What and

how?

Lorenzo Bergomi,

SOCIÉTÉ

GÉNÉRALE

Heston calibration

for counterparty

risk

Marco de

Innocentis,

CREDIT SUISSE


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13.05 Lunch Plus meet the speaker lunch tables (sign up required)

Munich

Lunchtable 1: Lorenzo Bergomi, Head of Quantitative Research, SOCIÉTÉ GÉNÉRALE

Lunchtable 2: David Leinweber, Founder, Center for Innovative Financial Technology, LAWRENCE BERKELEY

NATIONAL LABORATORY & Marcos López de Prado, Senior Lecturer, CORNELL UNIVERSITY

STREAM A

Algo Trading, E-Trading &

Machine Learning

Manhattan

STREAM B

Interest Rate

Modelling

Broadway

STREAM C

Option Pricing &

Volatility

Berlin

STREAM D

CCR, Collateral &

Central Clearing

Los Angeles

STREAM E Systematic

Investment

Strategies

Geneva

14.05 LOXM

developments:

Using deep

reinforcement

learning for

electronic trading

Vacslav Glukhov,

JPMORGAN

CHASE

Overcoming

negative rates in

yield curve

modelling

Michael Dempster,

UNIVERSITY OF

CAMBRIDGE

Cluster Induction

Peter Jaeckel, VTB CAPITAL

Behavioural

effects on XVA

Chris Kenyon, MUFG

SECURITIES

EMEA

Chairman’s opening remarks

Implementation

capacity of

systematic

investment

strategies

Maxim Kartamyshev, NORGES BANK INVESTMENT MANAGEMENT Tuomas Eerola, TECHILA TECHNOLOGIES

14.45 Universal features

of intraday price

formation: an

exploration via

Deep Learning

Rama Cont, IMPERIAL COLLEGE LONDON

Convexity with collateral switch/floor options, semi-analytic approach Emiliano Papa, DEUTSCHE BANK

Effective

approximations of

zero coupon

bond/survival

probabilities and

Arrow Debreu

Prices in short

rate models

Luca Capriotti,

CREDIT SUISSE

Counterparty Trading Limits Revisited: CSAs, IM, SwapAgent®, from PFE to PFL Chris Kenyon, MUFG SECURITIES EMEA

Trend following strategy: Adapting to regime change Arta Babaee, THAYER BROOK

PARTNERS LLP


15.55 Deep portfolio:

Using deep

learning for

portfolio

construction and

signal integration

Shilong Yang,

JPMORGAN

CHASE

Counterparty credit risk: A multi interest rate curve model for exposure modelling Andre Süss, CREDIT SUISSE

A new pricing

model for cash-

settled swaptions

Raoul Pietersz,

ABN AMRO

Credit and funding

risk associated

with CCPs: A

simple, robust

approach

Leif Andersen,

BANK OF

AMERICA

MERRILL LYNCH

Smart beta in

treasuries: Value and

momentum revisited

Riccardo Rebonato,

EDHEC

16.35 Volatility,

correlation &

market impact

microstructure

dynamics:

The fallacy of

using single stock

algos for portfolio

trading

Michael Steliaros, GOLDMAN SACHS

Random field

LIBOR market

model

developments

Tao Wu,

ILLINOIS

INSTITUTE OF

FINANCE

Swaptions, bonds

and equities in

HJM models

Viatcheslav

Belyaev,

ALLIANZ LIFE

Submodular risk allocation Samim Ghamami, U.S. DEPARTMENT OF THE TREASURY, OFR

Recent progress in

impact dynamics

Michael Benzaquen,

ECOLE

POLYTECHNIQUE


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17.15 Algorithmic

indices: How to

build strategies

matching the

views of the client

without any

overdose fitting

Adil Reghai,

NATIXIS

Variable annuities: Underlying risks and sensitivities Imad Chahboun, FEDERAL RESERVE BANK OF BOSTON

Quantum pricing

models –

Application of

infinite

dimensional group

representation in

derivative pricing

Gregory Pelts,

WELLS FARGO &

CO

Rethinking Market

Impact

Rama Cont,

IMPERIAL COLLEGE LONDON

Sustainable and

impact investing

Svetlana Borovkova,

VRIJE UNIVERSITEIT

AMSTERDAM

17.55 Chairman’s closing

remarks

Chairman’s closing

remarks


remarks


remarks


remarks

18.00 Networking drinks reception & champagne roundtable discussion groups - The Garden A chance for everyone to network and relax after the day’s presentations and discussions. Champagne discussion groups offer delegates a chance to delve deeper into timely topics of the day and


Roundtable 1: The information content of option prices led by Peter Carr, Department Chair,

Finance and Risk Engineering, NYU TANDON SCHOOL

Roundtable 2: Challenges facing AI in finance Led by Vacslav Glukhov, EMEA Linear Quantitative

Solutions, JPMORGAN CHASE

19.00 End of Main Conference Day 1


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Main Conference Day 2 Wednesday 16 May 2018


STREAM A

Innovations In Data, Modelling &

Quant Finance Manhattan

STREAM B

Volatility Modelling &

Trading Broadway

STREAM C

Regulation & FRTB

Berlin

STREAM D

Quant 2.0: Being A Quant In The New

Era Los Angeles

STREAM E

Computational &

Numerical Efficiency

Geneva

08.20 Chairman’s opening

remarks

Chairman’s

opening remarks


remarks


remarks


remarks

08.25 An extension of

the Heston Model

for P and Q

measure

modelling of FX

options

Riccardo

Rebonato,

EDHEC

On the joint

calibration of SPX

and VIX options

Julien Guyon,

BLOOMBERG

L.P.

Examining inconsistencies between regulatory risk capital demands and equity market capital in reality Dilip Madan, UNIVERSITY OF MARYLAND

Finance

applications of

machine learning

John Hull,

UNIVERSITY OF

TORONTO

Supercomputing and superintelligence Horst Simon, LAWRENCE BERKELEY NATIONAL LABORATORY

09.05 Deep primal-dual algorithm for BSDEs: Application of machine learning to CVA and IM Pierre Henry-

Labordere,

SOCIÉTÉ

GÉNÉRALE

Diamonds: A quant’s best friend Jim Gatheral, BARUCH COLLEGE, CUNY

The world of many curves and regulations Jessica James, COMMERZBANK AG

Machine learning and complex networks for stock market research Juho Kanniainen,

TAMPERE

UNIVERSITY OF

TECHNOLOGY

Advanced AAD applications for PDE and Monte Carlo pricing Luca Capriotti,

CREDIT SUISSE

09.45 A practitioner’s

view of machine

learning: Myth vs

reality

Stefano Pasquali,

BLACKROCK

Quantum bounds for option prices Paul McCloud, NOMURA

The revised Basel CVA framework Michael Pykhtin, FEDERAL RESERVE BOARD

Advances in

financial machine

learning

Marcos López de

Prado, CORNELL

UNIVERSITY

Recent progress in AAD tool development for C++ Uwe Naumann, RWTH AACHEN UNIVERSITY

10.25 Forecasting loan utilization using neural networks: quantifying the improvement of hidden layers Tore Opsahl, BANK OF AMERICA MERRILL LYNCH

Theta II – Barrier options Lorenzo Bergomi, SOCIÉTÉ GÉNÉRALE

Incorporating Basel

IV in KVA:

implementation

and impact

Matteo Rolle, LLOYDS BANKING GROUP

Blockchain: How to

practically use it in

financial markets?

Massimo Morini,

BANCA IMI

Getting to grips with Vectorisation


11.35 Regression and

information

criteria

Tyler Ward,

GOOGLE

Tough vol Jesper Andreasen, FORMER

DANSKE BANK

Banks as regulated traders Diana Iercosan, FEDERAL RESERVE BOARD

Managing quant minds Manlio Trovato, LLOYDS BANKING GROUP

New 4-factor model

with jumps-at-default

for pricing Quanto

CDS and an RBF

approach to solving

a system of 4D PDEs

Andrey Itkin,

BANK OF AMERICA

MERRILL LYNCH


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12.15 Quant vs. machine: Derivative pricing by machine learning Wim Schoutens, UNIVERSITY OF LEUVEN

Relative

valuation,

factoring linear

differential

operators, and

symplectic

geometry

Peter Carr,

NYU TANDON SCHOOL

Electronic and algorithmic trading risks and regulatory expectations

Risk measures for rogue traders with S-shaped utility Damiano Brigo,


Vectorised approach

to tree-based

machine learning

problems

Jan Novotny,

HSBC

12.55 Risk Modelling Best Practices From a CCP Perspective Udesh Jha,

CME Group

Rough volatility

and its

applications in FX

markets

Mikko Pakkanen,

Imperial College

London

Supervisory expectations on the internal model governance and embedding Vincent Sapin, NATIONAL BANK OF BELGIUM

Leveraging the

cloud for a more

collaborative

ecosystem

Felix Grevy,

FINASTRA

Financial cash-flow

scripting: Beyond

valuation

Antoine Savine,

DANSKE BANK

13.35 Lunch Plus meet the speaker lunch tables (sign up required)

Munich

Lunchtable 1: Pierre Henry-Labordere, Quant, Global Markets Quantitative Research, SOCIÉTÉ GÉNÉRALE

Lunchtable 2: Damiano Brigo, Chair and Co-Head of Group, Mathematical Finance, IMPERIAL COLLEGE

LONDON

Lunchtable 3: John Hull, Maple Financial Professor Of Derivatives & Risk Management at Joseph L. Rotman

School of Management, UNIVERSITY OF TORONTO

14.35 Machine learning, neural networks and NLP within derivatives pricing Youssef

Elouerkhaoui,

CITIGROUP

Stepping

Stochvol (SSV)

Peter Friz,

TU BERLIN,

WEIERSTRAß-

INSTITUT BERLIN

Chatham House

Rules

Validating and

backtesting models

to incorporate

dynamic IM into

regulatory capital

calculations

Andrew Hudson,

BANK OF

ENGLAND

How are quant teams positioning themselves as key enablers for business transformation

From artificial

intelligence to

machine learning,

from logic to

probability

Paul Bilokon,

THALESIANS LTD

15.15 Zero covariation

returns with

portfolio

constructions

based on support

vector machine

regressions

Dilip Madan, UNIVERSITY OF

MARYLAND

Volatility by

jumps

Laura Ballota,

CASS BUSINESS

SCHOOL

Bilateral margin requirements and the margin period of risk

Modelling and

hedging variable

annuity guarantees

– Challenges and

opportunities

Jeanine Kwong &

Tark Bouhennache,

MANULIFE

FINANCIAL

The rolling adjoints method: Fast greeks along Monte Carlo scenarios for early-exercise options Álvaro Leitao, UNIVERSITY OF BARCELONA



11

16.25 Deep Hedging Hans Buehler, JPMORGAN CHASE Lukas Gonon, ETH Zurich

From derivatives to derivatives on derivatives Bruno Dupire, BLOOMBERG L.P.

Applying machine learning to reduce the computational burden of FRTB IMA Chongxian Zhu, LLOYDS BANKING GROUP

Second

quantization of

banks

Christoph Burgard

BANK OF

AMERICA MERRILL

LYNCH

Approximation

Methods

Panel discussion

How useful are

asymptotics in

financial

engineering?

Moderator: Colin

Turfus, DEUTSCHE

BANK

Julien Hok, CREDIT AGRICOLE-

CIB

Romain Bompis, NATIXIS

17.05 CDS Rate

construction

method by

machine learning

techniques

Zhongmin Luo,

BIRKBECK,

UNIVERSITY OF

LONDON

Multi-asset stochastic volatility modelling Hamza Guennoun, SOCIÉTÉ GÉNÉRALE

How to define the

NMRF and how to

avoid extra

charges?

Nicolae Mera,

CREDIT SUISSE

Lessons from an

ethical hacker:

In a world of

unlimited data what

are the security

implications and

how can you be

continually vigilant

to cyber threats?

Freakyclown,

ETHICAL HACKER

AND SOCIAL

ENGINEER

Efficient pricing of

credit hybrid

derivatives

Colin Turfus,

DEUTSCHE BANK

17.45 Chaos and the

Garch

Fabio Mercurio,

BLOOMBERG L.P.

Local volatility model with stochastic interest rates and efficient calibration by PDE method Julien Hok, CREDIT AGRICOLE-CIB

Lending without banks Erik Vynckier, FORESTERS

FRIENDLY

SOCIETY

Fast analytical

approximations to

PDEs

Richard Martin,

APOLLO GLOBAL

MANAGEMENT

18.25 Chairman’s closing

remarks


remarks


remarks


remarks


remarks

18.30 Networking drinks reception & champagne roundtable discussion groups - Mediterranean A chance for everyone to network and relax after the day’s presentations and discussions. Champagne Discussion Groups offer delegates a chance to delve deeper into timely topics of the day and


Roundtable 1: Contributions of conic finance to asset allocation, hedging and trading Led by

Dilip Madan, Professor of Mathematical Finance, Robert H. Smith School of Business, UNIVERSITY

OF MARYLAND

Roundtable 2: Computational modelling with apache spark, Google cloud, and tensor flow led by

Tyler Ward, Local Search Modeler, GOOGLE

Roundtable 3: Machine learning meets quantitative finance, led by Hans Buehler, Global Lead for

Data Analytics in Markets, JPMORGAN CHASE

19.30 End of Main Conference Day 2


12

Main Conference Day 3 Thursday 17 May 2018


08.20 Chairman’s opening remarks New York

In the boardroom discussions

Geneva (Limited space: First-come first-served!)

08.30 Derivatives and the past and future of quantitative finance Breakthroughs in modern portfolio theory and the evolution of derivatives Emanuel Derman, Professor, COLUMBIA UNIVERSITY

09.10 High performance computing: Practical tools & real world applications What problems can high levels of computational performance solve and is it worth the investment?

Chairman Introduction XVA and P-measure

modelling

Andrey Chirikhin,

QUANTITATIVE

RECIPES

09.50 Cyber hacker address How I rob banks Hear anecdotes from a 20+ year career in ethical hacking, social engineering and physical assessments Freakyclown, Ethical Hacker and Social Engineer

Relative valuation

Peter Carr,

NYU TANDON SCHOOL

10.30 Hannibal ad Portas Impact of fintech on incumbent financial institutions Alexander Lipton, Founder and CEO, STRONGHOLD LABS

Future direction of

counterparty credit risk

models

Rajiv Sesodia,

STANDARD

CHARTERED BANK


STREAM A FX, Commodities & Trading

Innovations

Manhattan

STREAM B XVA Techniques &

Advancements

Broadway

STREAM C Risk Management, Model Risk &

Liquidity

Berlin

11.40 Chairman’s opening remarks Chairman’s opening remarks Chairman’s opening remarks

11.45 Identifying jumps in commodity

futures prices

Michael Dempster,

UNIVERSITY OF CAMBRIDGE

Panel discussion Capital & margin optimisation Moderator: Alexander Lipton, STRONGHOLD LABS Panellists: David Bachelier &

Gavin Jackson, CAPITALAB,

Adil Reghai, NATIXIS,

Andrew Green, SCOTIABANK

Alexandre Antonov, STANDARD CHARTERED

BANK

Model risk assessment: The model of models Eulogio Cuesta, SANTANDER

12.25 Cross currency derivatives

developments

KVA revisited Andrew Green, SCOTIABANK

Rates regimes for risk modelling Vinay Kotecha and Vladimir Chorniy, BNP PARIBAS

13.05 Lunch Plus meet the speaker lunch tables (sign up required) Munich

Lunchtable 1: Alexander Lipton, Founder and CEO, STRONGHOLD LABS

Lunchtable 2: Kathrin Glau, Lecturer in Financial Mathematics, QUEEN MARY UNIVERSITY OF LONDON

Lunchtable 3: Alexandre Antonov , Director, STANDARD CHARTERED BANK


13

14.05 Valuing a full requirements

contract as a real option by the

method of eigenclaims in the

non-markovian approach

Valery Kholodnyi,

WOLFGANG PAULI INSTITUTE

Algorithmic differentiation through least-squares Monte Carlo: CVA greeks and MVA for callables Andrew McClelland, NUMERIX

Learning the Optimal Risk

Marco Bianchetti and Marco Scaringi,

INTESA SANPAOLO

14.45 Using big data to trade FX Saeed Amen, CUEMACRO

Shih-Hau Tan, CUEMACRO

Capital Valuation Adjustment:

an indifference approach

Damiano Brigo,


World Cup: A quantitative analysis of

balance and fairness

Julien Guyon, BLOOMBERG L.P.

15.25 Can FX trading benefit from machine learning? Patrik Karlsson, SEB

Approximation methods for KVA under the final Basel III framework Rodney Hoskinson, ANZ BANK

Constructing a model inventory: Implications for reporting and compliance Alberto Elices, SANTANDER

16.05 Afternoon Tea & Networking Break

16.35 Implied volatility with bivariate Chebyshev interpolation Kathrin Glau, QUEEN MARY UNIVERSITY OF LONDON

Advanced techniques for SIMM-MVA calculations Alexandre Antonov, STANDARD CHARTERED BANK

What scenarios for consistent hedge &

VaR calculations?

Nadhem Meziou, NATIXIS

17.15 Managing risks of long term energy pricing agreements Paul Edge, EDP

Uncertainty quantification for

XVA applications

Stéphane Crépey,

UNIVERSITY OF EVRY

Bermudan swaption model risk

analysis: A local volatility approach

Juliusz Jabłecki,

NARODOWY BANK POLSKI

17.55 Chairman’s Closing Remarks Chairman’s Closing Remarks Chairman’s Closing Remarks

18.00 End of QuantMinds Main Conference


14

Technical Workshops Friday 18 May 2018Day

Hands-On Adjoint Coding The course provides a hands-on introduction to adjoint algorithmic differentiation (AAD). Both

manual coding of adjoints and the use of an operator overloading AAD tool for C++ (dco/c++) will be considered. Hybrid schemes include combinations of hand coding and use of operator

overloading as well as integration of local finite difference approximations (bumping) into adjoint code. Participants are encouraged to bring their laptops in order to draw full benefit from the interactive hands-on coding sessions. A C++ compiler should be installed. A trial version of dco/c++ will be distributed. The general adjoint code generation rules are formulated in a

language-independent fashion. We use C++ for examples including the main case study in form of a LIBOR market simulation.

(Berlin A room)

Workshop leaders: - Luca Capriotti, Managing Director - Head Quantitative Strategies Global Credit Products

EMEA, CREDIT SUISSE - Uwe Naumann, Professor of Computer Science, RWTH Aachen University


9.00 Workshop leaders' opening remarks

9.05 Fundamentals of adjoints in finance (Capriotti) Monte Carlo and pathwise derivative method, algebraic adjoint approaches, adjoint Algorithmic Differentiation (AAD), AAD and the pathwise derivative method, first applications. Hands-on exercise.


11.00 Hand-written adjoint of LIBOR model code (Naumann) Introducing LIBOR model and adjoint code generation rules, interactive / step by step manual coding of adjoint LIBOR code. Hands-on exercise.

12.30 Lunch

13.30 Further case studies and AAD for PDE methods (Capriotti) Case Study: Correlation Greeks for Basket Default Contracts, Case Study: Real Time Risk Management of Counterparty Risk and XVAs, Application to Partial Differential Equations, Market Prices Sensitivities, Calibration and the Implicit Function Theorem, Case Study: Market Prices Sensitivities of Default Intensity Models


15.30 Adjoints by overloading & application to LIBOR model code (Naumann) Introducing dco/c++, interactive / step by step dco/c++ adjoint of LIBOR code, hands-on exercise, outlook to advanced issues in AAD and dco/c++ support




15

Market Risk and the Fundamental Review of the Trading Book The Fundamental Review of the Trading Book is making the quantification of market risk for

regulatory purposes more sophisticated than ever before. This workshop will explain key concepts underlying both the standardized approach and the internal models approach. It will also discuss some of the more controversial aspects of FRTB such as the P&L attribution and

non-modellable risk factors.

(Berlin B room) Workshop leader:

John Hull, Maple Financial Professor Of Derivatives & Risk Management at Joseph L. Rotman School of Management, UNIVERSITY OF TORONTO


9.00 Workshop leaders' opening remarks

9.05 Risk measures

VaR and expected shortfall

Coherent risk measures

Allocation and aggregation of risk measures

Backtesting


11.00 Calculation methods

Historical simulation and its extensions

Stressed VaR and expected shortfall

Extreme value theory

Model Building approach and its use in SIMM and FRTB

12.30 Lunch

13.30 FRTB 1

The history: Basel I and Basel II.5

FRTB innovations: stressed expected shortfall and liquidity horizons

Standardised approach in FRTB

Weighted sensitivity approach


15.30 FRTB 2

Internal models approach in FRTB

Cascade approach

Trading book vs. banking book boundary

P&L attribution and backtesting




16

Modern Option Pricing

In this workshop we will address various aspects and techniques of modern option pricing. We will introduce mathematical tools, old and new, and explain how they can be used to solve

modern quantitative finance problems. The tools include McKean stochastic differential equations, backward stochastic differential equations (BSDEs), branching diffusions, linear programming, machine learning techniques, and optimal transport. They will be applied to a

variety of challenging issues that are crucial for risk-management and model risk assessment: the exact calibration of models to market smiles; the valuation of derivatives under parameter

uncertainty; the computation of the credit valuation adjustment (CVA) and initial margin (IM) of a large book of derivatives; and the derivation of model-independent bounds for option prices,

given the prices of vanilla options. Implementation details will be provided, together with

illustrative examples. (Geneva room)

- Pierre Henry-Labordere, Quant, Global Markets Quantitative Research, SOCIÉTÉ

GÉNÉRALE

- Julien Guyon, Senior Quant, BLOOMBERG L.P.


9.00 Workshop leader’s opening remarks

9.05 The particle method for smile calibration

Introductory example: Local stochastic volatility

The particle method: implementation details

Adding stochastic rates and stochastic repo/dividend yield

Path-dependent volatility

Local correlation

Cross-dependent volatility


11.00 Stochastic control techniques and applications

Hamilton-Jacobi-Bellman

Backward Stochastic Differential Equations

Uncertain volatility model

Uncertain default rate model

Different rates for borrowing and lending

Portfolio optimization

12.30 Lunch

1.30 Computing CVA and IM

The semilinear PDE for CVA and IM computations

Marked branching diffusions

Neural networks

Application to CVA and IM computations


3.30 Model-free bounds for option prices

Primal problem: Linear programming formulation

Dual problem: Optimal transport

Martingale optimal transport

Example: Bounds for VIX futures and VIX options given S&P 500 smiles



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