
Derivatives and Risk Management in Theory and Practice
1718 March 2008
Prof Claudio Albanese, Independent Consultant
Slides
Claudio Albanese has a PhD in Theoretical Physics from ETH Zurich
and has been working in Mathematical Finance since the mid 90s.
His academic career involve positions up to the rank of full professor.
He currently consults for several financial organizations in the area
of structured products and lectures extensively in professional conference and training circuits.
Claudio developed a framework for the mathematical finance of
long dated derivatives based on constructive probability theory and numerical linear algebra.
Abstract
LongTerm Options in Foreign Exchange and Interest Rate Markets
Long dated derivatives require a flexible modelling framework.
The econometrics challenge is to embed historical and crosssectional
estimations into derivative calibration. The engineering challenge is to structure a
model agnostic pricing engine whose performance depends only on the model size
but not on the process specification. The mathematical and numerical challenge
is to understand and use the smoothing mechanisms behind diffusion equations.
We illustrate through examples an efficient framework of this sort based
on direct kernel manipulations and operator algebraic methods.
We find that fully explicit discretization schemes provide a robust,
lownoise numerical valuation method for fundamental solutions of
diffusion equations and their derivatives.
Path dependent options are associated to an operator algebra
and can be classified into Abelian and nonAbelian:
blockdiagonalizations and moment methods apply to the first
and blockfactorization to the latter. Direct kernel manipulations
also allow one to correlate lattice models by means of dynamic
conditioning across even hundreds of factors without incurring
into the curse of dimensionality. Thanks to the internal smoothing mechanisms,
calculations are best executed in single precision floating point arithmetics
and staggering performance can be achieved by invoking BLAS Level3 routines
on massively parallel chipsets such as GPUs and the Cell BE.
Examples to be discussed include the swaption volatility cube calibration,
CMSs and CMS spreads, snowballs, PRDCs, FX linked range accruals and volatility derivatives.
The list is long but the model agnostic math and engineering is in common.
Dr Alexander Antonov, Numerix
Slides
Alexander Antonov got his PhD degree from the Landau Institute
for Theoretical Physics in 1997 and joined NumeriX LLC in 1998 where
he currently works as a Vice President Quantitative Research.
His activity is concentrated on modeling and numerical methods
for interest rates, cross currency, and credit. He is a regular
speaker for NumeriX at international conferences.
Abstract
Effective approximation of FX/EQ options for the hybrid models:
Heston and correlated Gaussian interest rates
We derive an effective approximation for FX/EQ options
for the Heston model coupled with correlated Gaussian interest rates.
The main technical result is an option evaluation for correlated
Heston/Lognormal processes. Unlike exactly solvable (affine) zero correlation case,
considered by J. Andreasen, nontrivial correlations
destroy affine structure / exact solvability.
Using powerful technique of the Markovian Projection
we come up with effective approximation and present its numerical confirmation.
Dr Oliver Caps, Dresdner Bank
Slides
Dr. Oliver Caps is a senior quantitative analyst
in the model validation team at Dresdner Bank
and develops valuation models for exotic interest rate and hybrid products.
He holds a Ph.D. in mathematics and an MBA.
Currently, his main interests are multifactor interest rate models
and smile modelling with stochastic volatility.
Abstract
Using CompilerEngineering Algorithms for Building Payoff Languages
During the last years the complexity of financial products has increased dramatically
(more complicated and/or hybrids payoffs) and the available development time
of pricing models for these products has decreased considerably.
Therefore, flexible payoff languages have gained importance in recent years
and one has to start thinking about parsing these languages.
But building a compiler is an old discipline in computer sciences and
powerful algorithms are provided.
In this talk we describe how some of these compiler engineering algorithms
can be used for building general payoff languages for interest rate and hybrids products.
The talk will focus on algorithmic aspects and IT implementation/design issues
will only be briefly sketched.
We will introduce basic concepts of compiler engineering (contextfree grammars, Chomsky Hierachy),
describe some parsing algorithms (topdown algorithms, bottom up algorithms, LL/LR Parser)
and compilergenerators (YACC, SPIRIT), and finally use these concepts to explain how
powerful payoff languages can be built to treat features like
 complex (and hybrid) payoffs depending on rates/FX/equity
 pathdependency like Cliquets or global floors
 trigger features like knockouts/autocallables/TARNs/switch products
 Bermudan callability
 range accruals
The talk is somehow a continuation of last year's talk.
While last year's talk focused on a flexible building block system for hybrid models,
this talk is concerned with a flexible building block system for hybrid products.
Dr Jürgen Hakala, Standard Chartered
Slides
Jürgen Hakala works at Standard Chartered Bank and is involved in foreign exchange,
as well as commodities, equity derivatives, and hybrids modeling.
He is interested in all aspects of computational finance and risk management.
He is, together with Uwe Wystup, the editor of a book about Foreign Exchange Risk.
Abstract
Foreign Exchange Derivatives: Market Conventions and Smile Dynamics
The conventions to quote the FX smile and the reasons why that
created problems in the past will be uncovered.
For the liquid G10 FX options markets the smile behaviour will
be characterized and models which aim at consistency with these
assumptions will be exhibited.
Dr Markus Himmerich, dfine
Slides
Dr Markus Himmerich is a Senior Consultant at dfine GmbH.
He worked on various projects on implementing risk management
and performance measurement systems at asset managers,
banks and industrial corporates.
He studied physics at the Johannes GutenbergUniversity of Mainz
and at the University of Bristol (UK) and mathematical finance
at the University of Oxford (UK).
The work presented here was part of a Master thesis done
in collaboration with Dr Aleksandar Mijatovic (Imperial College, London).
Abstract
The ContinuousTime Lattice Method  Option Pricing through Matrix Diagonalization
The ContinuousTime Lattice Method for the pricing of derivatives
can be applied if the underlying random process is a combination
of a diffusion and a jump process. Instead of approximating the
underlying process directly, the Markov generator of the process
is approximated on a lattice while the time variable stays continuous.
Using matrix diagonalization, the probability kernel of the underlying
random process is obtained and used for pricing.
This method was made popular in finance by Claudio Albanese.
We demonstrate the simplicity of this method by applying
it to the case of European vanilla options.
The underying process is a combination of the CEV and VarianceGamma Model.
The implied volatility smile of this model is obtained and shown to exhibit
an asymmetric smile and a flattening of the smile for longer times to maturity.
Fiodar Kilin, Quanteam AG
Slides
PhD student at Frankfurt School of Finance & Management.
Education: MSc in Applied Mathematics. Belarus State University (Minsk).
Work Experience: 2004present  Quanteam AG (Frankfurt), consultant.
Industrial projects: 2004present  Development of numerical pricing algorithms
for exotic equity derivatives (investment bank, ongoing project).
Research areas: Pricing and hedging of forwardskewsensitive equity derivatives. Model risk.
Abstract
Accelerating the Calibration of Stochastic Volatility Models
This paper compares the performance of three methods for
pricing vanilla options in models with known characteristic function:
(1) Direct integration, (2) Fast Fourier Transform (FFT), (3) Fractional FFT.
The most important application of this comparison is the choice
of the fastest method for the calibration of stochastic volatility models,
e.g. Heston, Bates, BarndorffNielsenShephard models or Levy models
with stochastic time. We show that using additional cache technique
makes the calibration with the direct integration method
at least seven times faster than the calibration with the fractional FFT method.
Available at Frankfurt School:
http://www.frankfurtschool.de/dms/publicationscqf/CPQF_Arbeits6/CPQF_Arbeits6.pdf
Dr Sven Ludwig, Sungard
Slides
Dr. Sven Ludwig is Manager Banking Central Europe at Sungard.
Abstract
Options Pricing  From Theory to Practice
This session covers important aspects when valuing vanilla and
exotic equity related derivatives in a real world trading environment.
Requirements are addressed on the pricing models when structuring,
quoting and managing risk. Solutions are outlined on how to handle
specific equity related aspects such as discrete dividends
and local volatilities when using finite difference and Monte Carlo methods.
Agenda:
 Sungard and FRONT ARENA (max 5 min)
 An overview of derivatives and valuation models.
 Requirements on option valuation models  Different aspects that are important when structuring, pricing,
quoting and managing risk of financial products.
 Finite difference methods  Issues related to American options and barrier options, dividend assumptions,
barrier features, local volatility, stable greeks and performance
Prof Antje Mahayni, University of DuisburgEssen
Slides
Professor Mahayni, born in 1971, studied economics at the University of Bonn.
In 2001, she finished her doctoral theses „Absicherungsstrategien auf unvollständigen Zinsmärkten“
(Hedging in Incomplete Markets) supervised by Prof. Dr. Dieter Sondermann.
From 2001 to 2006 she worked as an Assistant Professor with Prof. Dr. Klaus Sandman
and received her venia legendi at the University of Bonn with the postdoctoral thesis
„Risk Management of Minimum Return Guarantees and Embedded Options“.
Selected publications of Prof. Mahayni appeared in the Journal of Economic Dynamics and Control,
the German Economic Review and the Journal of Theoretical and Applied Finance. Prof.
Mahayni is a regular speaker at national and international conferences.
2003, she received an Outstanding Paper Award from the German Finance Association
for her contribution „The Risk Management of Minimum Return Guarantees“ (joined work with Erik Schlögl).
In 2007, Prof. Mahayni was appointed to the newly founded chair of
“Insurance and Risk Management” at the Mercator School of Management (University of DuisburgEssen).
Abstract
Effectiveness of CPPI Strategies under Discrete–Time Trading
The paper analyzes the effectiveness of the constant proportion portfolio
insurance (CPPI) method under trading restrictions. If the CPPI method
is applied in continuous time, the CPPI strategies provide a value above a
floor level unless the price dynamic of the risky asset permits jumps. The
risk of violating the floor protection is called gap risk. In practice, it is
caused by liquidity constraints and price jumps. Both can be modelled
in a setup where the price dynamic of the risky asset is described by a
continuous–time stochastic process but trading is restricted to discrete time.
We propose a discrete–time version of the continuous–time CPPI strategies
which satisfies three conditions. The resulting strategies are self–financing,
the asset exposure is non–negative and the value process converges. We
determine risk measures such as the shortfall probability and the expected
shortfall and discuss criteria which ensure that the gap risk does not increase
to a level which contradicts the original intention of portfolio insurance.
In addition, we introduce proportional transaction costs and analyze their
effects on the risk profile. This is joint work with
Sven Balder and Michael Brandl.
Dr Jan Maruhn, UniCredit Markets & Investment Banking
Slides
Jan Maruhn is working as a quantitative researcher in the
Financial Engineering Equities and Hybrids team
(Structured Products Development) of UniCredit Markets and Investment Banking.
He obtained his PhD in mathematics from the University of Trier, Germany.
His research interests include the application of nonlinear
and stochastic optimization techniques as well as
numerical methods in general to problems arising in mathematical finance.
Abstract
Selected Applications of Optimization in Finance
In recent years optimization has gained considerable importance
in the area of financial mathematics.
During this talk we highlight several classes of
optimization problems appearing in applications and discuss
their numerical solution. In a first part of the talk
we show how modern optimization algorithms can be used
for the efficient calibration of financial market models.
In case the model does not allow to derive a semiclosed form solution,
we show that adjointbased Monte Carlo methods can be employed
to successfully calibrate the model.
The second part of the talk discusses how optimization can
be applied to identify optimal hedging strategies,
with a particular focus on an uncertain
skew hedge for reverse barrier options.
Prof Hans Mittelmann, Arizona State University
Slides
Hans Mittelmann is a professor of Computational Mathematics at Arizona
State University. Prior to his appointment he was a professor at the
University of Dortmund. He has a PhD in Mathematics from the Technical
University of Darmstadt, where he also obtained the Habilitation. He has
written over 120 papers in Computational Mathematics and currently maintains
two of the most frequented websites in the area of optimization software.
His research has for more than 30 years been done in interdisciplinary
collaboration, lately nearly exclusively in optimization.
He is on the editorial board of several journals and book series including
Computational Management Science, Computational Optimization and
Applications, and International Series in Numerical Mathematics. Membership
in professional societies includes INFORMS, the Institute for Operations
Research and the Management Sciences and the Society for Industrial and
Applied Mathematics.
Abstract
Optimization Software for Financial Mathematics
Information about available software to solve a large variety of
optimization problems is provided at plato.asu.edu/guide.html
while some of this software is evaluated at plato.asu.edu/bench.html.
Starting with these sources an overview will be given on codes
that are particularly useful for applications in mathematical
finance.
Håkan Norekrans, Sungard
Slides
Håkan Norekrans is Product Manager SunGard FRONT ARENA,
responsible for pricing and risk management of equities and equity derivatives.
Abstract
Options Pricing  From Theory to Practice
This session covers important aspects when valuing vanilla and
exotic equity related derivatives in a real world trading environment.
Requirements are addressed on the pricing models when structuring,
quoting and managing risk. Solutions are outlined on how to handle
specific equity related aspects such as discrete dividends
and local volatilities when using finite difference and Monte Carlo methods.
Agenda:
 Sungard and FRONT ARENA (max 5 min)
 An overview of derivatives and valuation models.
 Requirements on option valuation models  Different aspects that are important when structuring, pricing,
quoting and managing risk of financial products.
 Finite difference methods  Issues related to American options and barrier options, dividend assumptions,
barrier features, local volatility, stable greeks and performance
Andrea Odetti, Commerzbank
Slides
Andrea Odetti is a quantitative analyst at Commerzbank Corporates and Markets
and is involved in equity derivatives as well as commodities modelling.
He holds a MSc in Probability and Finance from Universite Paris VI.
His main interests are correlation and smile modelling with stochastic volatility
and Levy models and numerical techniques for financial problems.
Abstract
High Performance Computing Techniques in Finance
We present a collection of techniques varying from the simple to the sophisticated.
We start by reviewing good coding practices. Then we illustrate that large benefits
are possible from reimplementation of core mathematical functions relevant to finance.
The next stage is to use hardware optimised libraries to
implement vectorised operations in time critical sections.
Finally we briefly review interpreted payoff languages
and show how we may "compile without compiling".
Prof Goran Peskir, University of Manchester
Goran Peskir holds a Chair in Probability at the University
of Manchester, where he is Head of Probability and Statistics,
currently comprising 20 specialists and 28 research students in
the field. Together with Albert N. Shiryaev he has coauthored
the book Optimal Stopping and FreeBoundary Problems which
describes the state of the art of optimal stopping and its
applications. His current research interests in Mathematical
Finance include Option Pricing Theory.
Abstract
The British Option
We present a new put/call option where the buyer may exercise at any
time prior to maturity whereupon his payoff is the `best prediction'
of the European payoff under the hypothesis that the true drift of
the stock price equals a contract drift. Inherent in this is the
protection feature which is key to the British option. Should the
option holder believe the true drift of the stock price to be
unfavourable (based upon the observed price movements), he can
substitute the true drift with the contract drift and minimise his
losses. With the contract drift properly selected the British put
option becomes a more `buyer friendly' alternative to the American
put: when stock price movements are favourable, the buyer may
exercise rationally to very comparable gains; when price movements
are unfavourable he is afforded the unique protection described
above. Moreover, the British put option is always cheaper than the
American put. In the final part we present a brief review of optimal
prediction problems which preceded the development of the British
option.
This is a joint work with F. Samee (Manchester).
Dr Kay Pilz, Sal. Oppenheim
Slides
Kay Pilz is working as a Quantitative Analyst for Sal. Oppenheim in Frankfurt.
His work and research interests focus mainly on the development
and implementation of equity as well as commodity models
for pricing and hedging derivative securities.
Kay graduated in mathematics from the University of Frankfurt
and holds a PhD in mathematical statistics from the University of Bochum.
Abstract
Option Pricing with NoArbitrage Constraints
In the absence of arbitrage opportunities,
theory imposes the price of a call to be a
two times continuously differentiable,
decreasing and convex function of the strike price
with additional bounds for the first and second derivative.
Hence, any reliable call price function or state
price density  the compounded second derivative  should
satisfy these constraints. Unfortunately,
it is usually not unproblematic to extract
this function from market data, since further influences
like bid/ask spreads and illiquidity distort the shape of the curve.
Nevertheless, for several applications,
like the calibration of local volatility functions,
it is important to know the call price function.
In this talk a new method for the estimation of the call
price function is proposed. The approach is nonparametric,
which means that no explicitly given form is assumed.
The call price function is estimated in a two stage procedure.
First, an estimation for the function from the market data
is performed such that monotonicity and convexity constraints are satisfied.
In a second step this estimate is modified with subject
to the boundary conditions for the derivatives.
It can be shown that under certain conditions
the estimation converges asymptotically almost surely to the true
call price function and the finite sample behavior
of this procedure is demonstrated by an application to real data.
Prof Eckhard Platen, Sydney University of Technology
Slides
Eckhard Platen is a Professor of Quantitative Finance
at the University of Technology, Sydney.
Prior to this appointment he was Head of the Centre of
Financial Mathematics in the Institute of Advanced Studies
at the Australian National University.
He has a PhD in Mathematics from the Technical University
in Dresden and obtained his Dr.sc. from the Academy of Sciences in Berlin.
He is coauthor of two books on numerical methods for
stochastic differential equations and has authored more
than hundred papers in applied mathematics and finance.
He serves on the editorial boards of four international journals
in finance and mathematics, including “Mathematical Finance”.
For over twenty five years he has worked on stochastic numerical methods
and has applied these methods successfully to many problems
in mathematical finance. His current research interests cover
areas ranging from financial market modeling, quantitative methods
in derivative pricing and risk analysis
to the statistics of stochastic processes in finance.
Abstract
The Law of the Minimal Price
The paper introduces a general market setting under which the Law of One Price
does no longer hold. Instead the Law of the Minimal Price will be derived,
which for a range of contingent claims provides lower prices than suggested
under the currently prevailing approach.
This new law only requires the existence of the numeraire portfolio,
which turns out to be the portfolio that maximizes expected logarithmic utility.
In several ways the numeraire portfolio cannot be outperformed by any nonnegative portfolio.
The new Law of the Minimal Price leads directly to the real world pricing formula,
which uses the numeraire portfolio as numeraire and the real world probability measure
as pricing measure when computing conditional expectations.
The pricing and hedging of extreme maturity bonds illustrates
that the price of a zero coupon bond, when obtained under the Law of the Minimal Price,
can be far less expensive than when calculated under the risk neutral approach.
Prof Rolf Poulsen, University of Copenhagen
Slides
Rolf Poulsen is a professor of mathematical finance at the University of Copenhagen
and is currently on a visiting sabbatical at the Gothenburg University.
He has a PhD in finance from the University of Aarhus.
His current research primarily focuses on practical hedging of exotic options,
model risk and optimal mortgage choice.
Abstract
AutoStatic for the People: RiskMinimizing Hedges of Barrier Options
We present a straightforward method for computing riskminimizing
static hedge strategies under general asset dynamics.
Experimental investigations for barrier options show
that in a stochastic volatility model with jumps the resulting hedges
have superior performance to previous suggestions in the literature.
We also illustrate that the riskminimizing static hedges work
in an infinite intensity Levydriven model,
and that the performance of the hedges are robust with respect to model risk.
This is joint work with Johannes Siven from Lund University.
Live link:
http://www.math.ku.dk/~rolf/Siven/AutoStatic.pdf
Dr Dietmar Schölisch, AXA
Slides
Dr. Dietmar Schölisch studied Economics and Business Administration,
majoring in Banking and Finance as well as Auditing.
The subject of his thesis was "Integrated value and risk management
of life insurers".
Dr. Schölisch is Vice Branch Manager of the German branch
of AXA Life Europe Ltd. and responsible for the dynamic hedging
of VA products of the AXA Group in Germany.
Since 1999 he has worked at various companies in the practical
side of investment controlling, primarily involved
in developing ALM systems and the management of bond/equity
portfolios using quantitative methods.
Abstract
Dynamic Hedging of Variable Annuities – TwinStar: The AXA Way
Looking at trends in the European life insurance market,
the ability of insurers to generate attractive risk/reward
profiles for their clients will obviously continue to
predominantly determine success in the retirement/savings market.
This stresses the importance of guarantees and other innovative,
flexible product features. At the same time however,
the importance of asset/liability management (ALM) techniques
has increased substantially due to lower risk capital levels
and the changing supervisory regime.
Under these circumstances, profitable growth with innovative products
will highly depend on the question which parts of the value
creation chain can be dealt with internally.
This may lead to a (re)shift of focus from macro to micro ALM
like when hedging embedded guarantees dynamically with derivatives.
Using AXA's TwinStar – the first variable annuity product
in the European market – as an example,
basic techniques for this shall be outlined.
Sanjeev Shukla, Commerzbank
Slides
Sanjeev Shukla is a quantitative analyst at Commerzbank Corporates and Markets, where he works in areas
of interest rate derivatives and hybrids. He holds an MSc from Cambridge University
in Pure and Applied Mathematics. His main areas of interest are rates and hybrid models,
smile modelling in hybrids and numerical techniques.
Abstract
High Performance Computing Techniques in Finance
We present a collection of techniques varying from the simple to the sophisticated.
We start by reviewing good coding practices. Then we illustrate that large benefits
are possible from reimplementation of core mathematical functions relevant to finance.
The next stage is to use hardware optimised libraries to
implement vectorised operations in time critical sections.
Finally we briefly review interpreted payoff languages
and show how we may "compile without compiling".
Dr Jianwei Zhu, LPA
Slides
Dr. Jianwei Zhu is currently a senior quant at LPA with focus
on equity/interest rate derivatives modelling.
He was a VP at Sal. Oppenheim and was responsible
for implementing interest rate /crossasset pricing library.
Prior to that, he was a senior quant for exotic equity derivatives
at WestLB in Düsseldorf. Dr. Jianwei Zhu began his career
in the model validation team at Dresdner Bank.
He holds a MSc in mathematic economics from University of Dortmund,
and a PhD in quantitative finance from University of Tübingen.
Dr. Jianwei Zhu published a book "Modular Pricing of Options"
in Springer Verlag, on the application of Fourier analysis
to stochastic volatilities, stochastic interest rates and random jumps.
Abstract
Generalized Swap Market Model and the Valuation of Interest Rate Derivatives
In this paper we will establish a generalized Swap Market Model (GSMM)
by unifying the stochastic processes of swap rates with constant tenors under
a single swap measure. GSMM is a natural extension of Libor Market Model (LMM)
for swap rates, and LMM can be considered as a special case of GSMM
since Libor is a special swap rate with the constant tenor of one period.
GSMM can be applied for pricing and hedging any interest rate derivatives,
and is suited especially for CMS and swap rate products.
There are a number of advantages of GSMM: (1). GSMM models swap rates directly,
and therefore achieves the best match between products and model. (2).
GSMM can be calibrated to the term structure of swaption volatilities
easily and quickly. (3) There is no translation of risk sensitivities
with respect to swap rates within GSMM. In contrast, risk sensitives
such as Vega for swap rates can not be derived directly,
and must be translated in an inefficient, inaccurate and
nontransparent manner in the most existing interest rate models.
(4) All smile modelings for LMM can be taken over for GSMM since
GSMM and LMM share an almost identical mathematical structure.
(5) GSMM avoids the inconsistency of the market conventions
in cap and swaptions markets. Accompanied by these favourite features,
GSMM should be a promising interest rate model for pricing and
hedging most traded swap rate structures in financial market.
Available at SSRN: http://ssrn.com/abstract=1028710
