Good deals in markets with frictions

This paper studies a portfolio choice problem such that the pricing rule may incorporate transaction costs and the risk measure is coherent and expectation bounded. We will prove the necessity of dealing with pricing rules such that there are essentially bounded stochastic discount factors, which must be also bounded from below by a strictly positive value. Otherwise good deals will be available to traders, i.e., depending on the selected risk measure, investors can build portfolios whose (risk, return) will be as close as desired to (- infinite, + infinite) or (0, infinite). This pathologic property still holds for vector risk measures (i.e., if we minimize a vector valued function whose components are risk measures). It is worthwhile to point out that essentially bounded stochastic discount factors are not usual in financial literature. In particular, the most famous frictionless, complete and arbitrage free pricing models imply the existence of good deals for every coherent and expectation bounded measure of risk, and the incorporation of transaction costs will no guarantee the solution of this caveatRisk measure, Perfect and imperfect markets, Stochastic discount factor, Portfolio choice model, Good deal

CAPM and APT-like models with risk measures.

The paper deals with optimal portfolio choice problems when risk levels are given by coherent risk mea sures, expectation bounded risk measures or general deviations. Both static and dynamic pricing models may be involved. Unbounded problems are characterized by new notions such as (strong) compatibility between prices and risks. Surprisingly, the lack of bounded optimal risk and/or return levels arises for important pricing models (Black and Scholes) and risk measures (VaR, CVaR, absolute deviation, etc.). Bounded problems present a Market Price of Risk and generate a pair of benchmarks. From these bench marks we introduce APT and CAPM like analyses, in the sense that the level of correlation between every available security and some economic factors explains the security expected return. The risk level non correlated with these factors has no influence on any return, despite the fact that we are dealing with risk functions beyond the standard deviation.Risk measure; Compatibility between prices and risks; Efficient portfolio; APT and CAPM-like models;

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;

Stability of the optimal reinsurance with respect to the risk measure

The optimal reinsurance problem is a classic topic in Actuarial Mathematics. Recent approaches consider a coherent or expectation bounded risk measure and minimize the global risk of the ceding company under adequate constraints. However, there is no consensus about the risk measure that the insurer must use, since every risk measure presents advantages and shortcomings when compared with others. This paper deals with a discrete probability space and analyzes the stability of the optimal reinsurance with respect to the risk measure that the insurer uses. We will demonstrate that there is a “stable optimal retention” that will show no sensitivity, insofar as it will solve the optimal reinsurance problem for many risk measures, thus providing a very robust reinsurance plan. This stable optimal retention is a stop-loss contract, and it is easy to compute in practice. A fast algorithm will be given and a numerical example presented.Optimal reinsurance, Risk measure, Sensitivity, Stable optimal retention, Stop-loss reinsurance

Stable solutions for optimal reinsurance problems involving risk measures.

The optimal reinsurance problem is a classic topic in actuarial mathematics. Recent approaches consider a coherent or expectation bounded risk measure and minimize the global risk of the ceding company under adequate constraints. However, there is no consensus about the risk measure that the insurer must use, since every risk measure presents advantages and shortcomings when compared with others. This paper deals with a discrete probability space and analyzes the stability of the optimal reinsurance with respect to the risk measure that the insurer uses. We will demonstrate that there is a ‘‘stable optimal retention’’ that will show no sensitivity, insofar as it will solve the optimal reinsurance problem for many risk measures, thus providing a very robust reinsurance plan. This stable optimal retention is a stop-loss contract, and it is easy to compute in practice. A fast linear time algorithm will be given and a numerical example presented.Optimal reinsurance; Risk measure; Sensitivity; Stable optimal retention; Stop-loss reinsurance;

Coherent Pricing

Recent literature proved the existence of an unbounded market price of risk (MPR) or maximum generalized Sharpe ratio (GSR) if one combines the most important Brownian-motion-linked arbitrage free pricing models with a coherent and expectation bounded risk measure. Furthermore, explicit sequences of portfolios with a theoretical (risk, return) diverging to (��1;+1) were constructed and their performance tested. The empirical evidence revealed that the divergence to (��1;+1) is only theoretical (not real), but the MPR is much larger than the GSR of the most important international stock indices. The natural question is how to modify the available pricing models so as to prevent the caveat above. The theoretical MPR cannot equal inf nity but must be large enough (consistent with the empirical findings) and this will be the focus of this paper. It will be shown that every arbitrage free pricing model can be improved in such a manner that the new stochastic discount factor (SDF) satisfie the two requirements above, and the newMPR becomes bounded but large enough. This is important for several reasons; Firstly, if the existent models predict unrealistic price evolutions then these mistakes may imply important capital losses to practitioners and theoretical errors to researchers. Secondly, the lack of an unbounded MPR is much more coherent and consistent with equilibrium. Finally, the major discrepancies between the initial pricing model and the modifie one will affect the tails of their SDF, which seems to justify several empirical caveats of previous literature. For instance, it has been pointed out that it is not easy to explain the real quotes of many deeply OTM options with the existing pricing models

Capital requirements, good deals and portfolio insurance with risk measures

General risk functions are becoming very important for managers, regulators and supervisors. Many risk functions are interpreted as initial capital requirements that a manager must add and invest in a risk-free security in order to protect the wealth of his clients. This paper deals with a complete arbitrage free pricing model and a general expectation bounded risk measure, and it studies whether the investment of the capital requirements in the risk-free asset is optimal. It is shown that it is not optimal in many important cases. For instance, if the risk measure is the CV aR and we consider the assumptions of the Black and Scholes model. Furthermore, in this framework and under short selling restrictions, the explicit expression of the optimal strategy is provided, and it is composed of several put options. If the confidence level of the CV aR is close to 100% then the optimal strategy becomes a classical portfolio insurance. This theoretical result seems to be supported by some independent and recent empirical analyses. If there are no limits to sale the risk-free asset, i.e., if the manager can borrow as much money as desired, then the framework above leads to the existence of “good deals” (i.e., sequences of strategies whose V aR and CV aR tends to minus infinite and whose expected return tends to plus infinite). The explicit expression of the portfolio insurance strategy above has been used so as to construct effective good deals. Furthermore, it has been pointed out that the methodology allowing us to build portfolio insurance strategies and good deals also applies for pricing models beyond Black and Scholes, such as Heston and other stochastic volatility model

CAPM-like formulae and good deal absence with ambiguous setting and coherent risk measure

Risk measures beyond the variance have shown theoretical advantages when addressing some classical problems of Financial Economics, at least if asymmetries and/or heavy tails are involved. Nevertheless, in portfolio selection they have provoked several caveats such as the existence of good deals in most of the arbitrage free pricing models. In other words, models such as Black and Scholes or Heston allow investors to build sequences of strategies whose expected return tends to in nite and whose risk remains bounded or tends to minus in nite. This paper studies whether this drawback still holds if the investor is facing the presence of multiple priors, as well as the properties of optimal portfolios in a good deal free ambiguous framework. With respect to the rst objective, we show that there are four possible results. If the investor uncertainty is too high he/she has no incentives to buy risky assets. As the uncertainty (set of priors) decreases the interest in risky securities increases. If her/his uncertainty becomes too low then two types of good deal may arise. Consequently, there is a very important di¤erence between the ambiguous and the non ambiguous setting. Under ambiguity the investor uncertainty may increase in such a manner that the model becomes good deal free and presents a market price of risk as close as possible to that re ected by the investor empirical evidence. Hence, ambiguity may help to overcome some meaningless ndings in asset pricing. With respect to our second objective, good deal free ambiguous models imply the existence of a benchmark generating a robust capital market line. The robust (worst-case) risk of every strategy may be divided into systemic and speci c, and no robust return is paid by the speci c robust risk. A couple of betas may be associated with every strategy, and extensions of the CAPM most important formulas will be proved.Partially supported by "RD-Sistemas S.A", Welzia Management SGIIC SA and "MICINN" (Spain, Grant ECO2009-14457-C04

Good deals in markets with frictions

This paper studies a portfolio choice problem such that the pricing rule may incorporate transaction costs and the risk measure is coherent and expectation bounded. We will prove the necessity of dealing with pricing rules such that there are essentially bounded stochastic discount factors, which must be also bounded from below by a strictly positive value. Otherwise good deals will be available to traders, i.e., depending on the selected risk measure, investors can build portfolios whose (risk, return) will be as close as desired to (- infinite, + infinite) or (0, infinite). This pathologic property still holds for vector risk measures (i.e., if we minimize a vector valued function whose components are risk measures). It is worthwhile to point out that essentially bounded stochastic discount factors are not usual in financial literature. In particular, the most famous frictionless, complete and arbitrage free pricing models imply the existence of good deals for every coherent and expectation bounded measure of risk, and the incorporation of transaction costs will no guarantee the solution of this cavea

Golden options in financial mathematics

This paper deals with the construction of smooth good deals (SGD), i.e., sequences of self- nancing strategies whose global risk diverges to ∞ and such that every security in every strategy of the sequence is a smooth derivative with a bounded delta. If the selected risk measure is the value at risk then these sequences exist under quite weak conditions, since one can involve risks with both bounded and unbounded expectation, as well as non-friction-free pricing rules. Moreover, every strategy in the sequence is composed of an European option plus a position in a riskless asset. The strike of the option is easily computed in practice, and the ideas may also apply in some actuarial problems such as the selection of an optimal reinsurance contract. If the chosen risk measure is a coherent one then the general setting is more limited. Indeed, though frictions are still accepted, expectations and variances must remain nite. The existence of SGDs will be characterized, and computational issues will be properly addressed as well. It will be shown that SGDs often exist, and for the conditional value at risk they are composed of the riskless asset plus easily replicable European puts. Numerical experiments will be presented in all of the studied cases.This research was partially supported by the University Carlos III of Madrid (Project 2009/00445/002) and the Spanish Royal Academy of Sciences. The usual caveat applies