Past Nomura Seminar

27 November 2014
16:00
to
17:30
Professor Denis Talay
Abstract

(Denis Talay, Inria — joint works with N. Champagnat, N. Perrin, S. Niklitschek Soto)

In this lecture we present recent results on SDEs with weighted local times and discontinuous coefficients. Their solutions allow one to construct probabilistic interpretations of  semilinear PDEs with discontinuous coefficients and transmission boundary conditions in terms of BSDEs which do not satisfy classical conditions.

13 November 2014
16:00
to
17:30
Professor Dr. Denis Belomestny
Abstract

In this talk we consider optimal stopping problems under a class of coherent risk measures which includes such well known risk measures as weighted AV@R or absolute semi-deviation risk measures. As a matter of fact, the dynamic versions of these risk measures do not have the so-called time-consistency property necessary for the dynamic programming approach. So the standard approaches are not applicable to optimal stopping problems under coherent risk measures. In this paper, we prove a novel representation, which relates the solution of an optimal stopping problem under a coherent risk measure to the sequence of standard optimal stopping problems and hence makes the application of the standard dynamic programming-based approaches possible. In particular, we derive the analogue of the dual representation of Rogers and Haugh and Kogan. Several numerical examples showing the usefulness of the new representation in applications are presented as well.

6 November 2014
16:00
to
17:30
Dr. Gonçalo dos Reis
Abstract

We investigate the effects of a finite set of agents interacting socially in an equilibrium pricing mechanism. A derivative written on non-tradable underlyings is introduced to the market and priced in an equilibrium framework by agents who assess risk using convex dynamic risk measures expressed by Backward Stochastic Differential Equations (BSDE). An agent is not only exposed to financial and non-financial risk factors, but he also faces performance concerns with respect to the other agents. The equilibrium analysis leads to systems of fully coupled multi-dimensional quadratic BSDEs.

Within our proposed models we prove the existence and uniqueness of an equilibrium. We show that aggregation of risk measures is possible and that a representative agent exists. We analyze the impact of the problem's parameters in the pricing mechanism, in particular how the agent's concern rates affect prices and risk perception.

31 October 2014
16:00
to
17:30
Dr. Olivier Guéant
Abstract

When firms want to buy back their own shares, they often use the services of investment banks through ASR contracts. ASR contracts are execution contracts including exotic option characteristics (an Asian-type payoff and Bermudian/American exercise dates). In this talk, I will present the different types of ASR contracts usually encountered, and I will present a model in order to (i) price ASR contracts and (ii) find the optimal execution strategy for each type of contract. This model is inspired from the classical (Almgren-Chriss) literature on optimal execution and uses classical ideas from option pricing. It can also be used to price options on illiquid assets. Original numerical methods will be presented.

23 October 2014
16:00
to
17:30
Professor Piotr Fryzlewicz
Abstract
This is an exploratory talk in which we describe different potential 
uses of randomness in time series analysis.

In the first part, we talk about Wild Binary Segmentation for change-point detection, where randomness is used as a device for sampling from the space of all possible contrasts (change-point detection statistics) in order to reduce the computational complexity from cubic to just over linear in the number of observations, without compromising on the accuracy of change-point estimates. We also discuss an interesting related measure of change-point certainty/importance, and extensions to more general nonparametric problems.

In the second part, we use random contemporaneous linear combinations of time series panel data coming from high-dimensional factor models and argue that this gives the effect of "compressively sensing" the components of the multivariate time series, often with not much loss of information but with reduction in the dimensionality of the model.

In the final part, we speculate on the use of random filtering in time series analysis. As an illustration, we show how the appropriate use of this device can reduce the problem of estimating changes in the autocovariance structure of the process to the problem of estimating changes in variance, the latter typically being an easier task.
 
16 October 2014
16:00
to
17:30
David Shelton
Abstract

From a theoretical point of view, theta is a relatively simple quantity: the rate of change in value of a financial derivative with respect to time. In a Black-Scholes world, the theta of a delta hedged option can be viewed as `rent’ paid in exchange for gamma. This relationship is fundamental to the risk-management of a derivatives portfolio. However, in the real world, the situation becomes significantly more complicated. In practice the model is continually being recalibrated, and whereas in the Black-Scholes world volatility is not a risk factor, in the real world it is stochastic and carries an associated risk premium. With the heightened interest in automation and electronic trading, we increasingly need to attempt to capture trading, marking and risk management practice algorithmically, and this requires careful consideration of the relationship between the risk neutral and historical measures. In particular these effects need to be incorporated in order to make sense of theta and the time evolution of a derivatives portfolio in the historical measure. 

19 June 2014
16:00
to
17:30
Gilles Pages
Abstract
This is a joint work with V. Lemaire (LPMA-UPMC). We propose and analyze a Multilevel Richardson-Romberg (MLRR) estimator which combines the higher order bias cancellation of the Multistep Richardson-Romberg ($MSRR$) method introduced in~[Pag\`es 07] and the variance control resulting from the stratification in the Multilevel Monte Carlo (MLMC) method (see~$e.g.$ [Heinrich 01, M. Giles 08]). Thus we show that in standard frameworks like discretization schemes of diffusion processes, an assigned quadratic error $\varepsilon$ can be obtained with our (MLRR) estimator with a global complexity of $\log(1/\varepsilon)/\varepsilon^2$ instead of $(\log(1/\varepsilon))^2/\varepsilon^2$ with the standard (MLMC) method, at least when the weak error $\E Y_h-\EY_0}$ induced by the biased implemented estimator $Y_h$ can be expanded at any order in $h$. We analyze and compare these estimators on several numerical problems: option pricing (vanilla or exotic) using $MC$ simulation and the less classical Nested Monte Carlo simulation (see~[Gordy \& Juneja 2010]).

Pages