1,049 research outputs found
Very low-protein diet to postpone renal failure: Pathophysiology and clinical applications in chronic kidney disease
No full text: The uremic syndrome is a metabolic disorder characterized by the impairment of renal handling of several solutes, the resulting accumulation of toxic products and the activation of some adaptive but detrimental mechanisms which all together contribute to the progression of renal damage. In moderate to advanced renal failure, the dietary manipulation of nutrients improves metabolic abnormalities and may contribute to delay the time of dialysis initiation. This commentary focuses on the physiopathological rationale and the clinical application of the very low-protein diet supplemented with ketoanalogs for the management of chronic kidney disease
Real-world effectiveness of sucroferric oxyhydroxide for serum phosphorus control in dialysis patients: an interim subgroup analysis of the verifie study
No full textERA-EDTA CONGRESS (55th, 2018, Copenhagen Denmark)Francisco, A. de, Fouque, D., Boletis, I., Vervloet, M., Kalra, P., Ketteler, M., Messa, P., Stauss-Grabo, M., Derlet, A., Rakov, V., Walpen, S., Perrin, A., Ficociello, L., Cannata-Andia, J., Wanner, C., Rottembourg, J
Real-world safety and effectiveness of sucroferric oxyhydroxide in dialysis patients: an interim analysis of the verifie study
No full textERA-EDTA CONGRESS (55th, 2018, Copenhagen Denmark)Fouque, D., Boletis, I., Francisco, A. de, Vervloet, M., Kalra, P., Ketteler, M., Messa, P., Stauss-Grabo, M., Derlet, A., Rakov, V., Walpen, S., Perrin, A., Ficociello, L., Rottembourg, J., Cannata-Andia, J., Wanner, C
Probability Distribution and Option Pricing for Drawdown in a Stochastic Volatility Environment ( Revised in May 2009; Electronic version of an article will be published in "International Journal of Theoretical and Applied Finance". [copyright world Scientific Publishing Company][http://www.worldscinet.com/ijtaf/] )
No full textThis paper studies the probability distribution and option pricing for drawdown in a stochastic volatility environment. Their analytical approximation formulas are derived by the application of a singular perturbation method (Fouque et al. [7]). The mathematical validity of the approximation is also proven. Then, numerical examples show that the instantaneous correlation between the asset value and the volatility state crucially affects the probability distribution and option prices for drawdown.
"Probability Distribution and Option Pricing for Drawdown in a Stochastic Volatility Environment"
No full textThis paper studies the probability distribution and option pricing for drawdown in a stochastic volatility environment. Their analytical approximation formulas are derived by the application of a singular perturbation method (Fouque et al. [7]). The mathematical validity of the approximation is also proven. Then, numerical examples show that the instantaneous correlation between the asset value and the volatility state crucially affects the probability distribution and option prices for drawdown.
A unified approach to systemic risk measures via acceptance sets
No full textWe specify a general methodological framework for systemic risk measures via multi-dimensional acceptance sets and aggregation functions. Existing systemic risk measures can usually be interpreted as the minimal amount of cash needed to secure the system after aggregating individual risks. In contrast, our approach also includes systemic risk measures that can be interpreted as the minimal amount of cash that secures the aggregated system by allocating capital to the single institutions before aggregating the individual risks.
An important feature of our approach is the possibility of allocating cash according to the future
state of the system (scenario-dependent allocation). We illustrate with several examples the advantages of this feature.
We also provide conditions which ensure monotonicity, convexity, or quasi-convexity of our systemic risk measures
Heston stochastic vol-of-vol model for joint calibration of VIX and S&P 500 options
No full textA parsimonious generalization of the Heston model is proposed where the volatility-of-volatility is assumed to be stochastic. We follow the perturbation technique of Fouque et al [Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives, 2011, Cambridge University Press] to derive a first-order approximation of the price of options on a stock and its volatility index. This approximation is given by Heston’s quasi-closed formula and some of its Greeks. It can be efficiently calculated since it requires to compute only Fourier integrals and the solution of simple ODE systems. We exemplify the calibration of the model with S&P 500 and VIX data
Small-time asymptotics for fast mean-reverting stochastic volatility models
No full textIn this paper, we study stochastic volatility models in regimes where the maturity is small, but large compared to the mean-reversion time of the stochastic volatility factor. The problem falls in the class of averaging/homogenization problems for nonlinear HJB-type equations where the "fast variable" lives in a noncompact space. We develop a general argument based on viscosity solutions which we apply to the two regimes studied in the paper. We derive a large deviation principle, and we deduce asymptotic prices for out-of-the-money call and put options, and their corresponding implied volatilities. The results of this paper generalize the ones obtained in Feng, Forde and Fouque [SIAM J. Financial Math. 1 (2010) 126-141] by a moment generating function computation in the particular case of the Heston model.
On fairness of systemic risk measures
No full textIn our previous paper “A unified approach to systemic risk measures via acceptance sets” (Mathematical Finance, 2018), we have introduced a general class of systemic risk measures that allow random allocations to individual banks before aggregation of their risks. In the present paper, we prove a dual representation of a particular subclass of such systemic risk measures and the existence and uniqueness of the optimal allocation related to them.We also introduce an associated utility maximisation problem which has the same solution as the minimisation problem associated to the systemic risk measure. In addition, the optimiser in the dual formulation provides a risk allocation which is fair from the point of view of the individual financial institutions. The case with exponential utilities which allows explicit computation is treated in detail
Multivariate systemic risk measures and computation by deep learning algorithms
No full textIn this work, we propose deep learning-based algorithms for the computation of systemic shortfall risk measures defined via multivariate utility functions. We discuss the key related theoretical aspects, with a particular focus on the fairness properties of primal optima and associated risk allocations. The algorithms we provide allow for learning primal optimizers, optima for the dual representation and corresponding fair risk allocations. We test our algorithms by comparison to a benchmark model, based on a paired exponential utility function, for which we can provide explicit formulas. We also show evidence of convergence in a case in which explicit formulas are not available
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