20,017 research outputs found
Minimizing measures of risk by saddle point conditions.
The minimization of risk functions is becoming a very important topic due to its interesting applications in Mathematical Finance and Actuarial Mathematics. This paper addresses this issue in a general framework. Many types of risk function may be involved. A general representation theorem of risk functions is used in order to transform the initial optimization problem into an equivalent one that overcomes several mathematical caveats of risk functions. This new problem involves Banach spaces but a mean value theorem for risk measures is stated, and this simplifies the dual problem. Then, optimality is characterized by saddle point properties of a bilinear expression involving the primal and the dual variable. This characterization is significantly different if one compares it with previous literature. Furthermore, the saddle point condition very easily applies in practice. Four applications in finance and insurance are presented.Risk minimization; Saddle point condition; Actuarial and finantial aplications;
Minimizing measures of risk by saddle point conditions
The minimization of risk functions is becoming a very important topic due to its interesting
applications in Mathematical Finance and Actuarial Mathematics. This paper addresses
this issue in a general framework. Many types of risk function may be involved. A
general representation theorem of risk functions is used in order to transform the initial
optimization problem into an equivalent one that overcomes several mathematical caveats
of risk functions. This new problem involves Banach spaces but a mean value theorem
for risk measures is stated, and this simplifies the dual problem. Then, optimality is
characterized by saddle point properties of a bilinear expression involving the primal and
the dual variable. This characterization is significantly different if one compares it with
previous literature. Furthermore, the saddle point condition very easily applies in practice.
Four applications in finance and insurance are presented.This research was partially supported by āāWelzia Management SGIIC SA, RD_Sistemas SAāā and āāMEyCāā (Spain), Grant ECO2009-14457-C04.Publicad
A Model of Anticipated Regret and Endogenous Beliefs
This paper clarifies and extends the model of anticipated regret and endogenous beliefs based on the Savage (1951) Minmax Regret Criterion developped in Suryanarayanan (2006a). A decision maker chooses an action with state contingent consequences but cannot precisely assess the true probability distribution of the state. She distrusts her prior about the true distribution and surrounds it with a set of alternative but plausible probability distributions. The decision maker minimizes the worst expected regret over all plausible probability distributions and alternative actions, where regret is the loss experienced when the decision maker compares an action to a counterfactual feasible alternative for a given realization of the state. Preliminary theoretical results provide a systematic algorithm to find the solution to the decision problem and show how models of Minmax Regret differs from models of ambiguity aversion and expected utility. In particular, the solution to the decision problem can always be represented as a saddle point solution to an equivalent zerosum game problem. This new problem jointly produces the solution to the Anticipated Regret problem and the endogenous belief. We then use the endogenous belief to define the implicit certainty equivalent and to build an infinite horizon and time consistent problem for a decision maker minimizing her lifetime worst expected regrets.
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
We describe and develop a close relationship between two problems that have
customarily been regarded as distinct: that of maximizing entropy, and that of
minimizing worst-case expected loss. Using a formulation grounded in the
equilibrium theory of zero-sum games between Decision Maker and
Nature, these two problems are shown to be dual to each other, the solution
to each providing that to the other. Although Tops\oe described this connection
for the Shannon entropy over 20 years ago, it does not appear to be widely
known even in that important special case. We here generalize this theory to
apply to arbitrary decision problems and loss functions. We indicate how an
appropriate generalized definition of entropy can be associated with such a
problem, and we show that, subject to certain regularity conditions, the
above-mentioned duality continues to apply in this extended context.
This simultaneously provides a possible rationale for maximizing entropy and
a tool for finding robust Bayes acts. We also describe the essential identity
between the problem of maximizing entropy and that of minimizing a related
discrepancy or divergence between distributions. This leads to an extension, to
arbitrary discrepancies, of a well-known minimax theorem for the case of
Kullback-Leibler divergence (the ``redundancy-capacity theorem'' of information
theory). For the important case of families of distributions having certain
mean values specified, we develop simple sufficient conditions and methods for
identifying the desired solutions.Comment: Published by the Institute of Mathematical Statistics
(http://www.imstat.org) in the Annals of Statistics
(http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000055
Local Volatility Calibration by Optimal Transport
The calibration of volatility models from observable option prices is a
fundamental problem in quantitative finance. The most common approach among
industry practitioners is based on the celebrated Dupire's formula [6], which
requires the knowledge of vanilla option prices for a continuum of strikes and
maturities that can only be obtained via some form of price interpolation. In
this paper, we propose a new local volatility calibration technique using the
theory of optimal transport. We formulate a time continuous martingale optimal
transport problem, which seeks a martingale diffusion process that matches the
known densities of an asset price at two different dates, while minimizing a
chosen cost function. Inspired by the seminal work of Benamou and Brenier [1],
we formulate the problem as a convex optimization problem, derive its dual
formulation, and solve it numerically via an augmented Lagrangian method and
the alternative direction method of multipliers (ADMM) algorithm. The solution
effectively reconstructs the dynamic of the asset price between the two dates
by recovering the optimal local volatility function, without requiring any time
interpolation of the option prices
- ā¦