Rheological properties of fresh and cryopreserved human arteries tested in vitro

Cryopreservation is widely used to preserve blood vessels for a while but is controversially suspected to affect the mechanical behavior of these allografts. The aim of this study was to determine whether differences in the three-dimensional mechanical behavior exist or not between fresh and cryopreserved arteries. Using a previously developed experimental system, in vitro inflation tests were performed on twenty segments of human fresh and cryopreserved arteries, in static conditions. Opening angles were also measured from images of rings in zero-stress state. The initial reference state was chosen as the unloaded state and tests were performed on specimens stretched at natural “in vivo” length. Mechanical measures calculated are “natural” (Hencky) strains (finite deformations), “true” (Cauchy) stresses in radial, circumferential, and longitudinal directions as well as strain energy per unit volume. Tangent moduli are derived from radial and circumferential stress-strain characteristics using non-linear curve fitting. Values of incremental and pressure-strain elastic parameters, wall stiffness, and compliance per unit length are also calculated. Results are presented in terms of characteristics of stresses and strains in the three directions, axial force, tangent moduli vs strains or stresses, and energy per unit volume, for both types of artery, with reference to transmural pressure. Detailed numerical results are given at mean transmural pressure or in the physiological range. Significant differences are indicated by statistic Student T-tests. Results obtained show that significant differences exist between rheological properties of fresh and cryopreserved segments of human artery. Strains, stresses, axial force, strain energy, and wall stiffness values highlight those differences whereas elastic parameters, compliance, and opening angle do not. The usefulness of some parameters to compare the mechanical behavior existing between fresh and cryopreserved arteries is therefore underlined. Received: 3 January 2000 Revision received: 12 April 2000 Accepted: 8 May 2000     

Optimal posting price of limit orders: learning by trading

We model a trader interacting with a continuous market as an iterative algorithm that adjusts limit prices at a given rhythm and propose a procedure to minimize trading costs. We prove the $a.s.$ convergence of the algorithm under assumptions on the cost function and give some practical criteria on model parameters to ensure that the conditions to use the algorithm are met (notably, using the co-monotony principle). We illustrate our results with numerical experiments on both simulated and market data.     

Dealing with the inventory risk: a solution to the market making problem

Market makers continuously set bid and ask quotes for the stocks they have under consideration. Hence they face a complex optimization problem in which their return, based on the bid-ask spread they quote and the frequency at which they indeed provide liquidity, is challenged by the price risk they bear due to their inventory. In this paper, we consider a stochastic control problem similar to the one introduced by Ho and Stoll (J Fin Econ 9(1): 47–73, 1981) and formalized mathematically by Avellaneda and Stoikov (Quant Fin 8(3):217–224, 2008). The market is modeled using a reference price S _t following a Brownian motion with standard deviation σ, arrival rates of buy or sell liquidity-consuming orders depend on the distance to the reference price S _t and a market maker maximizes the expected utility of its P&L over a finite time horizon. We show that the Hamilton–Jacobi–Bellman equations associated to the stochastic optimal control problem can be transformed into a system of linear ordinary differential equations and we solve the market making problem under inventory constraints. We also shed light on the asymptotic behavior of the optimal quotes and propose closed-form approximations based on a spectral characterization of the optimal quotes.     

Mean field game of controls and an application to trade crowding

In this paper we formulate the now classical problem of optimal liquidation (or optimal trading) inside a mean field game (MFG). This is a noticeable change since usually mathematical frameworks focus on one large trader facing a “background noise” (or “mean field”). In standard frameworks, the interactions between the large trader and the price are a temporary and a permanent market impact terms, the latter influencing the public price. In this paper the trader faces the uncertainty of fair price changes too but not only. He also has to deal with price changes generated by other similar market participants, impacting the prices permanently too, and acting strategically. Our MFG formulation of this problem belongs to the class of “extended MFG”, we hence provide generic results to address these “MFG of controls”, before solving the one generated by the cost function of optimal trading. We provide a closed form formula of its solution, and address the case of “heterogenous preferences” (when each participant has a different risk aversion). Last but not least we give conditions under which participants do not need to instantaneously know the state of the whole system, but can “learn” it day after day, observing others’ behaviors.     

Efficiency of the price formation process in presence of high frequency participants: a mean field game analysis

This paper deals with a stochastic order-driven market model with waiting costs, for orderbooks with heterogenous traders. Offer and demand of liquidity drives price formation and traders anticipate future evolutions of the orderbook. The natural framework we use is mean field game theory, a class of stochastic differential games with a continuum of anonymous players. Several sources of heterogeneity are considered including the mean size of orders. Thus we are able to consider the coexistence of Institutional Investors and high frequency traders (HFT). We provide both analytical solutions and numerical experiments. Implications on classical quantities are explored: orderbook size, prices, and effective bid/ask spread. According to the model, in markets with Institutional Investors only we show the existence of inefficient liquidity imbalances in equilibrium, with two symmetrical situations corresponding to what we call liquidity calls for liquidity. During these situations the transaction price significantly moves away from the fair price. However this macro phenomenon disappears in markets with both Institutional Investors and HFT, although a more precise study shows that the benefits of the new situation go to HFT only, leaving Institutional Investors even with higher trading costs.