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26 - 27 March 2012
Saeed Amen, Nomura & Thalesians Ltd
Saeed started his career at Lehman Brothers. He worked on the FX desk developing systematic trading models for
both G10 and EM and was part of the team who developed the MarQCuS suite of models. He was also responsible for
a systematic FX prop trading book and conducted research around high frequency FX including economic events. He
currently works at Nomura as a Vice President in Quantitative Strategy, also in FX, developing their model infrastructure.
He also covers gold for Nomura and his work on gold has been quoted by ZeroHedge, WSJ and La Republica. He graduated from
Imperial College with a first class honours master's degree in Mathematics and Computer Science. Saeed Amen is a Managing
Director at Thalesians Ltd also.
Abstract
What drives gold?
We discuss the various historical drivers for gold, looking at factors such as real rates, relationship
with USD and also risk sentiment. We shall discuss the decomposition of end user demand for gold and the
impact of factors on gold such as increases in margin requirements, central bank buying and QE. We examine
the various possible scenarios for the gold price over the coming years, with a focus on our own view.
Peter Austing, Quantitative Analytics, Barclays Capital
Peter moved from mathematical physics to finance in 2004. He has been in his current role in
the quantitative analytics team at Barclays Capital for five years, and is particularly interested
in correlation and volatility modeling for foreign exchange derivatives.
Abstract
Valuing Basket Options with Asset and Correlation Smiles
We show how to value basket options with both asset smiles and correlation smiles. The instrument
best used to mark the implied correlation smile depends on the asset class. For equity baskets,
two-asset sub-baskets can be used, and we provide an analytic on-smile valuation for N-asset baskets.
For FX-baskets, cross-vanillas determine the correlation smiles, and we provide a semi-analytic valuation.
Effectiveness of the method is demonstrated by comparing against an alternative fully dynamic (but heavy) model.
Dr. Eric Benhamou, Pricing Partners
Eric Benhamou is the CEO of Pricing Partners, an international software developer
of derivatives pricing analytics and a service provider of independent valuation for all OTC derivatives.
Current coverage includes interest rates, credit, equity, inflation, foreign exchange, commodities, insurance
derivatives and hybrids.
Eric Benhamou is also known for its endeavor to gather financial institutions, start-ups and public research
centers on collaborative innovation in financial mathematics. Previously, he headed the fixed income quantitative
research at Ixis CIB, joining from Goldman Sachs. He is a regular speaker at professional conferences and has
published various articles on subjects like advanced Monte Carlo simulation, inflation derivatives and other
option pricing results. A former alumnus of the Ecole Polytechnique, the ENSAE, he holds a Ph.D. in financial
mathematics from the London School of Economics.
Abstract
Prof. Jin-Chuan Duan, National University of Singapore
Duan is the Director of Risk Management Institute at the National University of Singapore (NUS) and concurrently holds the
Cycle & Carriage Professorship in Finance at the NUS Business School. He is an Academician of Academia Sinica and also holds a
visiting distinguished research chair at National Taiwan University. Duan completed his undergraduate education at the National
Taiwan University, an MBA from the State University of New York at Albany and a PhD in Finance from the University of Wisconsin-Madison.
He specializes in financial engineering and risk management, and is known for his work on the GARCH option pricing model. He has authored
numerous scholarly publications on derivative securities and risk management, and written a book and occasional media commentaries on current
financial/economic events. Before joining the NUS, Duan held the Manulife Chair Professorship at the Rotman School of Management, University
of Toronto, and also once taught at the Hong Kong University Science and Technology and McGill University. Duan is spearheading a non-profit
credit research initiative launched in 2009, which pioneers a "public good" approach to credit rating reform via a Wikipedia-style model
development undertaking. The initiative currently provides daily updated default forecasts for over 28,000 exchange-listed firms in 30
economies in Asia, North America and Europe, and the usage is free ( http://www.rmi.nus.edu.sg/cri/ ).
Abstract
Dynamic Default Predictions and a Bottom-Up Approach to Credit Portfolio Management
This talk comprises two parts. First, the forward intensity method for corporate default predictions proposed by Duan,
Sun and Wang (2011) will be introduced with discussions on its conceptual foundation, econometric formulation, implementation
issues and empirical findings on the US data. The talk will also touch upon the role of momentum in default prediction and a
useful distance-to-default treatment for financial firms if one wants to include them in the sample. The forward intensity
method powers the default prediction system of the non-profit Credit Research Initiative (CRI) by the Risk Management
Institute of National University Singapore. The CRI currently produces daily updated default predictions, from one month
to two years ahead, for about 30,000 exchange-listed firms in 30 economies in Asia, North America and Europe. In the second
part of the talk, I will show how one can utilize the freely accessible CRI infrastructure for credit portfolio management.
Since the forward intensity approach considers all obligors jointly and dynamically, it naturally forms a bottom-up approach
to modelling credit portfolios. An example will be used to demonstrate this application.
Prof. Matthias Fengler, University St. Gallen (HSG)
Matthias Fengler is Assistant Professor of Financial Econometrics at the School of Economics and Political Science of the University St. Gallen (HSG).
Before joining St. Gallen University, he spent six years as a senior quantitative analyst in the investment
banking branch of Sal. Oppenheim jr. & Cie, Frankfurt. His primary fields of expertise are modeling equity derivatives
and financial statistics and econometrics. Matthias Fengler earned his PhD in Quantitative Finance at the Humboldt-Universität,
Berlin. He is author of the textbook Semiparametric Modelling of Implied Volatility edited by the Springer-Verlag.
Abstract
Semi-Nonparametric Estimation of the Call Price Surface Under No-Arbitrage Constraints
When studying the economic content of cross sections of option price data, researchers either explicitly or
implicitly view the discrete ensemble of observed option prices as a realization from a smooth surface defined
across exercise prices and expiry dates. Yet despite adopting a surface perspective for estimation, it is common
practice to infer the option pricing function, for each expiry date separately, slice by slice. In this paper,
we suggest a semi-nonparametric estimator for the entire call price surface based on a tensor-product B-spline.
To enforce no-arbitrage constraints in strike and calendar dimension we establish sufficient no-arbitrage conditions
on the control net of the tensor product (TP) B-spline. Since these conditions are independent of the degrees of the
underlying polynomials, the estimator can be parametrized with TP B-splines of arbitrary order. As example we estimate
a smooth call price surface from S&P500; option quotes. From this estimate we obtain families of state price densities
and empirical pricing kernels and a local volatility surface.
Dr. Jörg Kienitz, Deutsche PostBank
Joerg Kienitz is the head of Quantitative Analysis at Deutsche Postbank AG. He is primarily
involved in the development and implementation of models for pricing structured products,
derivatives and asset allocation. He authored a number of quantitative finance papers and his
book on Monte Carlo frameworks has been published in 2009 with Wiley. He is member of the editorial
board of International Review of Applied Financial Issues and Economics. Joerg holds a Ph.D.
in stochastic analysis and probability theory.
Abstract
Adjoint Methods
Dr. Alexander Langnau, Allianz
Alex Langnau is Global Head of Quantitative Analytics at Allianz
Investment Management. He is also Visiting Scientist at the Ludwig-
Maximillian University Munich. Prior to this he held various roles across
the industry including Global Head of Quants across asset classes at Dresdner Bank,
Global Head of Equity Derivatives Modelling at Merrill Lynch and Global Head of Exotic
Equity Derivatives Modelling at Goldman Sachs. He started his career as a member of
the Global Analytics team at Bakers Trust/Deutsche Bank. He holds a PhD in Theoretical
Physics from the Stanford Linear Accelerator Center and completed his post-doc in the
area of Theoretical Particle Physics at Cornell University. His current research interests
include dynamic modelling of correlations and high frequency trading strategies.
Abstract
Marking systemic RISK in the Merton model
The downside risk of a portfolio of (equity)assets is generally substantially higher than the downside
risk of its components. In particular in times of crises when assets tend to have high correlation, the
understanding of this difference can be crucial in managing systemic risk of a portfolio. In this paper
we generalize Merton's option formula in the presence jumps to the multi-asset case. It is shown how
common jumps across assets provide an intuitive and powerful tool to describe systemic risk that is
consistent with data. The methodology provides a new way to mark and risk-manage systemic risk of
portfolios in a systematic way.
Roger Lee, University of Chicago
Roger Lee is Associate Professor of Mathematics
at the University of Chicago. Previously he held
postdoctoral positions at Stanford University and NYU,
and worked in Global Equity-Linked Products at Merrill Lynch
in New York. His recent publications address
robust approaches to pricing/hedging,
asymptotics of implied volatility,
and trading of realized volatility.
He has a PhD from Stanford University
and a BA from Harvard University.
Abstract
Asymptotics of Implied Volatility to Arbitrary Order
In a unified model-free framework that includes long-expiry,
short-expiry, extreme-strike, and jointly-varying strike-expiry regimes,
we find asymptotic implied volatility and implied variance formulas in
terms of L, with rigorous error estimates of order 1/L to any given power,
where L denotes the absolute log of an option price that approaches zero.
Our results therefore sharpen, to arbitrarily high order of accuracy, the
model-free asymptotics of implied volatility in extreme regimes. We then
apply these general formulas to particular examples: Levy and Heston.
Joint work with Kun Gao.
Dr. Owen Matthews, Fintegral
Owen Matthews studied physics at Leicester, in the UK, and performed research in theoretical astrophysics in
Switzerland, Germany and India. He joined Fintegral in 2010 and is now a senior consultant specializing in stress
testing and model validation for banks in Europe and the Middle East. He has implemented the stress-testing software
for large and complex banking books, including the development of special treatments for sectors such as sovereigns.
Abstract
From theory to practice – Project experience on designing and implementing a Universal Bank comprehensive Stress Testing framework
We will present an example of a complete end-to-end Stress Testing implementation project and discuss the
various challenges that had to be met for a large universal bank. The discussion will first focus on the
design and integration of the various Risk & Finance elements that have to be included in a comprehensive
and compliant Stress Testing framework. We will then move on to discuss the challenges and implications
involved in embedding Stress and Scenario Testing in core bank processes and linking it to Risk Appetite
in a practical example.
Dr. Attilio Meucci, Kepos Capital, LP & SYMMYS.com
Attilio Meucci is a pioneer in advanced risk and portfolio management. His innovations include Entropy Pooling
(technique for fully flexible portfolio construction), Factors on Demand (on-the-fly factor model for optimal hedging),
Effective Number of Bets (entropy-eigenvalue statistic for diversification management), Fully Flexible Probabilities
(technique for on-the-fly stress-test and estimation without re-pricing), and Copula-Marginal Algorithm (algorithm to
generate panic copulas). Attilio is the founder of SYMMYS, under whose umbrella he designed and teaches the six-day ARPM Bootcamp,
and manages the charity One More Reason.
Attilio Meucci serves as the chief risk officer at Kepos Capital LP.
Attilio is the author of Risk and Asset Allocation - Springer and numerous other publications in practitioner and academic journals.
He holds a BA summa cum laude in Physics from the University of Milan, an MA in Economics from Bocconi University, a PhD in Mathematics
from the University of Milan and is a CFA chartholder.
Abstract
Prof. Dr. Cornelis Oosterlee, Technical University of Delft
Prof. Cornelis Oosterlee is a full professor in Applied Mathematics at the Delft University of Technology,
the Netherlands, and he works as a group leader at the CWI, Centre for Mathematics and Computer Science in Amsterdam.
His main field of research is Computational Finance, where he has cooperations with the Dutch financial industry.
He is an associate editor for the Journal of Computational Finance.
He obtained his PhD from Delft University of Technology in 1993; spent 8 years in Germany at the National Research Center
for Mathematics and Computer Science, in Sankt Augustin after that. He is a full professor since 2007.
Oosterlee is teaching computational finance courses in Delft; he teaches Fourier methods in the MSc courses on
numerical methods in finance at Oxford University, and taught a summer school in Tokyo in 2009, and in Cape Town in 2012.
Abstract
On the applicability of the COS method for exotic option pricing
In this presentation we will present our follow-up research, after the introduction of the COS method.
The COS method is an efficient pricing method for financial derivatives, based on Fourier cosine expansions
and the availability of the characteristic function. The method is being used by the financial industry within
the calibration process for the pricing of European options.
Next to this kind of options, we recently succeeded to price Bermudans, arithmetic Asian options, multi-asset
options (all with L'evy processes for the underlying) but also inflation options, the latter based on hybrid stochastic dynamics.
In this presentation we will report on the extension of the COS method to solving these kinds of exotic products.
Professor Dr. Ekkehard Sachs, University of Trier
Ekkehard Sachs is a Professor at the University of Trier and previously has held positions at Virginia Tech and
North Carolina State University. He is an expert in numerical methods for optimization problems, in particular
with partial differential equations and serves on various editorial boards of international journals in optimization.
He has published three books and more than 100 research papers. His interest in finance is in calibration and hedging
of options and, in particular, the numerical aspects of these tasks.
Abstract
Erik Vynckier, Scottish Widows Investment Partnership
Investment Director at the Financial Solutions Group of SWIP (Scottish Widows Investment Partnership), designing and
implementing asset management and risk management for life insurance and pension fund clients, normally involving
derivatives and innovative asset strategies.
Prior to SWIP, Erik worked at Credit Suisse First Boston in equity program trading, quantitative modeling of derivatives
pricing and hedging, and asset-liability management for European life & pension clients; at HSBC in asset-liability
structuring for the European life & pensions clients; and at Standard Life (Group) supporting the UK, German, Canadian and
Hong Kong subsidiaries covering with-profits, fixed and variable annuities and guaranteed products. Erik commenced his
career with positions in research & development and process engineering in the oil and chemical industry in Germany, the
United States and the United Kingdom and has an MSc Chemical Engineering of the Rijksuniversiteit Gent (Belgium),
Post-Doctoral research at the Institut Français du Pétrole (Lyon, France) and an MBA from the London Business School.
Abstract
The Power of Dataflow Computing in Financial Engineering
Case Studies in Acceleration of Credit, Equity and Interest Rates Models.
The paper reviews algorithms to achieve ultra-high speed computation in financial engineering through
dataflow computing at a considerable cost and time savings to the computer grids of today's datacentres.
In fact, beyond getting computations done faster, the driving case is to move from overnight computations
to real-time live updates with the market.
Dataflow computation streams the data through a computational pipeline, with results flowing out with every
clocktick: a 200 Mhz pipeline enables up to 200 million results per second, further enhanced by potentially
fitting hundreds of pipelines on a single node. The software model for dataflow computing centers around describing
dataflow graphs spatially rather than the temporal programming in a multi-threaded environment on CPUs,
The case studies presented here rely on the Maxeler suite of acceleration tools, in particular the MaxSpot
profiler for statically and dynamically analysing code during the design phase of the algorithm and MaxCompiler
creating binary dataflow pipelines from a Java representation.
Earlier successes in credit tranching (integrating the Gaussian copula with stochastic recovery, using pipelined
fast Fourier transforms) and in equity derivatives (with pipelined random number generation for Heston's volatility
process and for the Poisson jumps of the spot equity process) are reported, quoting achieved field-performance
compared to single- or multi-threaded algorithms on multi-core Intel CPU. Original extensions to Cox-Ingersoll-Ross
and to Stochastic Alpha Beta Rho (SABR) interest rate models are now presented to the MathFinance conference.
The ease of extending these models to stochastic local volatility using pipelined function evaluation - without
loss of performance - is explained. The paper sketches the implementation of Forward Libor market models in a computational
pipeline, a crucial breakthrough for rates trading desks, for whom real-time computation and calibration has so far hindered
the widespread application of the Libor market model.
See presentation by JP Morgan at Stanford!
Joint paper with Oskar Mencer (Maxeler, Imperial College London) & Robin Bruce (Maxeler).
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