Francois Watier

Francois Watier
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Francois verified their affiliation via an institutional email.
Verified
Francois verified their affiliation via an institutional email.
  • Ph.D. Mathematics (Sherbrooke)
  • Professor (Full) at University of Quebec in Montreal

About

12
Publications
1,943
Reads
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38
Citations
Current institution
University of Quebec in Montreal
Current position
  • Professor (Full)
Additional affiliations
University of Quebec in Montreal
Position
  • Professor (Associate)
Education
May 1998 - November 2002
Université de Sherbrooke
Field of study
  • Probability

Publications

Publications (12)
Article
A continuous-time utility portfolio selection problem is studied in a market in which the interest rate, appreciation rates and volatility coefficients are driven by Brownian motion. We construct an optimal portfolio using results from forward-backward stochastic differential equations (FBSDE) theory. As an illustration, exact computation of the op...
Article
We study a mean–variance investment problem in a continuous-time framework where the interest rates follow Cox–Ingersoll–Ross dynamics. We construct a mean–variance efficient portfolio through the solutions of backward stochastic differential equations. We also give sufficient conditions under which an explicit analytic expression is available for...
Article
In this paper, we establish closed-form formulas for key probabilistic properties of the cone-constrained optimal mean-variance strategy, in a continuous market model driven by a multidimensional Brownian motion and deterministic coefficients. In particular, we compute the probability to obtain to a point, during the investment horizon, where the a...
Article
Full-text available
In this paper, we develop a Monte Carlo based algorithm for estimating the FPT (first passage time) density of the solution of a one-dimensional time-homogeneous SDE (stochastic differential equation) through a time-dependent frontier. We consider Brownian bridges as well as local Daniels curve approximations to obtain tractable estimations of the...
Article
Li and Zhou (2006) established that an investor, following an unconstrained mean-variance strategy, will achieve its discounted targeted wealth with a probability greater than 80%. Surprisingly, we will show that under short-selling restrictions (i.e without the possibility of borrowing stocks) this lower bound probability still holds.
Article
In this paper, we develop a Monte Carlo based algorithm for estimating the FPT density of a time-homogeneous SDE through a time-dependent frontier. We consider Brownian bridges as well as localized Daniels curve approximations to obtain tractable estimations of the FPT probability between successive points of a simulated path of the process. Under...
Article
Full-text available
In this work, we study the goal-achieving probabilities of a multiperiod mean-variance financial strategy under a \emph{switch-when-safe} stopping time rule. This stopping time is defined as the first moment, if it occurs, where the investor's cumulative wealth, at this point, can be safely reinvested in a simple bank account in order to meet his f...
Article
Full-text available
We establish, through solving semi-infinite programming problems, bounds on the probability of safely reaching a de- sired level of wealth on a finite horizon, when an investor starts with an optimal mean-variance financial investment strategy under a non-negative wealth restriction.
Article
Full-text available
The present paper offers a closed-form solution to an unconstrained multi-period mean-variance problem when the investor's portfolio consists of a single stock and bond and where only fairly general conditions are imposed on these assets. As the reader will see, among the advantages of the proposed solution one finds that it is general enough to al...
Article
Thèse (Ph. D.)--Université de Sherbrooke, 2003. Comprend des réf. bibliogr.

Questions

Question (1)
Question
I wish to efficiently generate random samples from the five-parameter generalized hyperbolic distribution (which includes the hyperbolic and normal inverse gaussian distribution). I would greatly appreciate obtaining MatLab source codes :)

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