The Experts below are selected from a list of 447981 Experts worldwide ranked by ideXlab platform
Rishi Talreja - One of the best experts on this subject based on the ideXlab platform.
-
a Stochastic Model for order book dynamics
Operations Research, 2010Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a continuous-time Stochastic Model for the dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics, and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo Stock Exchange. Using simple matrix computations and Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the midprice, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy based on our results.
-
a Stochastic Model for order book dynamics
Social Science Research Network, 2008Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a Stochastic Model for the continuous-time dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo stock exchange. Using Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the mid-price, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy that is based on our results.
Alberto Bemporad - One of the best experts on this subject based on the ideXlab platform.
-
dynamic option hedging with transaction costs a Stochastic Model predictive control approach
International Journal of Robust and Nonlinear Control, 2019Co-Authors: Mogens Graf Plessen, Laura Puglia, Tommaso Gabbriellini, Alberto BemporadAbstract:Summary This paper proposes Stochastic Model predictive control as a tool for hedging derivative contracts (such as plain vanilla and exotic options) in the presence of transaction costs. The methodology combines Stochastic scenario generation for the prediction of asset prices at the next rebalancing interval with the minimization of a Stochastic measure of the predicted hedging error. We consider 3 different measures to minimize in order to optimally rebalance the replicating portfolio: a trade-off between variance and expected value of hedging error, conditional value at risk, and the largest predicted hedging error. The resulting optimization problems require solving at each trading instant a quadratic program, a linear program, and a (smaller-scale) linear program, respectively. These can be combined with 3 different scenario generation schemes: the lognormal stock Model with parameters recursively identified from data, an identification method based on support vector regression, and a simpler scheme based on perturbation noise. The hedging performance obtained by the proposed Stochastic Model predictive control strategies is illustrated on real-world data drawn from the NASDAQ-100 composite, evaluated for a European call and a barrier option, and compared with delta hedging.
-
Stochastic Model predictive control for constrained discrete time markovian switching systems
Automatica, 2014Co-Authors: Panagiotis Patrinos, Pantelis Sopasakis, Haralambos Sarimveis, Alberto BemporadAbstract:In this paper we study constrained Stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for Stochastic Model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.
-
scenario based Stochastic Model predictive control for dynamic option hedging
Conference on Decision and Control, 2010Co-Authors: Alberto Bemporad, Laura Puglia, Tommaso Gabbriellini, Leonardo BellucciAbstract:For a rather broad class of financial options, this paper proposes a Stochastic Model predictive control (SMPC) approach for dynamically hedging a portfolio of underlying assets. By employing an option pricing engine to estimate future realizations of option prices on a finite set of one-step-ahead scenarios, the resulting Stochastic optimization problem is easily solved as a least-squares problem at each trading date with as many variables as the number of traded assets and as many constraints as the number of predicted scenarios. After formulating the dynamic hedging problem as a Stochastic control problem, we test its ability to replicate the payoff at expiration date for plain vanilla and exotic options. We show not only that relatively small hedging errors are obtained in spite of price realizations, but also that the approach is robust with respect to market Modeling errors.
Rama Cont - One of the best experts on this subject based on the ideXlab platform.
-
a Stochastic Model for order book dynamics
Operations Research, 2010Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a continuous-time Stochastic Model for the dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics, and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo Stock Exchange. Using simple matrix computations and Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the midprice, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy based on our results.
-
a Stochastic Model for order book dynamics
Social Science Research Network, 2008Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a Stochastic Model for the continuous-time dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo stock exchange. Using Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the mid-price, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy that is based on our results.
Michael Beer - One of the best experts on this subject based on the ideXlab platform.
-
the role of the bhattacharyya distance in Stochastic Model updating
Mechanical Systems and Signal Processing, 2019Co-Authors: Matteo Broggi, Michael BeerAbstract:Abstract The Bhattacharyya distance is a Stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation Model updating framework, in which the Bhattacharyya distance is fully embedded. The Bhattacharyya distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the Stochastic updating by utilizing the superior features of the Bhattacharyya distance, representing a convenient, efficient, and capable metric for Stochastic Model updating and uncertainty characterization.
Sasha Stoikov - One of the best experts on this subject based on the ideXlab platform.
-
a Stochastic Model for order book dynamics
Operations Research, 2010Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a continuous-time Stochastic Model for the dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics, and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo Stock Exchange. Using simple matrix computations and Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the midprice, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy based on our results.
-
a Stochastic Model for order book dynamics
Social Science Research Network, 2008Co-Authors: Rama Cont, Sasha Stoikov, Rishi TalrejaAbstract:We propose a Stochastic Model for the continuous-time dynamics of a limit order book. The Model strikes a balance between three desirable features: it can be estimated easily from data, it captures key empirical properties of order book dynamics and its analytical tractability allows for fast computation of various quantities of interest without resorting to simulation. We describe a simple parameter estimation procedure based on high-frequency observations of the order book and illustrate the results on data from the Tokyo stock exchange. Using Laplace transform methods, we are able to efficiently compute probabilities of various events, conditional on the state of the order book: an increase in the mid-price, execution of an order at the bid before the ask quote moves, and execution of both a buy and a sell order at the best quotes before the price moves. Using high-frequency data, we show that our Model can effectively capture the short-term dynamics of a limit order book. We also evaluate the performance of a simple trading strategy that is based on our results.
