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Optimal mean-variance robust hedging under asset price model misspecification

by T. Toronjadze - Georgian Math. J
"... Abstract. The problem of constructing robust optimal in the mean-variance sense trading strategies is considered. The approach based on the notion of sensitivity of a risk functional of the problem w.r.t. small perturbation of asset price model parameters is suggested. The optimal mean-variance robu ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. The problem of constructing robust optimal in the mean-variance sense trading strategies is considered. The approach based on the notion of sensitivity of a risk functional of the problem w.r.t. small perturbation of asset price model parameters is suggested. The optimal mean-variance

Low-Power CMOS Digital Design

by Anantha P. Chandrakasan, Samuel Sheng, Robert W. Brodersen - JOURNAL OF SOLID-STATE CIRCUITS. VOL 27, NO 4. APRIL 1992 413 , 1992
"... Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput. Techniques for low-power operation are shown which use the ..."
Abstract - Cited by 580 (20 self) - Add to MetaCart
the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. An architectural-based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations. This optimum is achieved

A New Extension of the Kalman Filter to Nonlinear Systems

by Simon J. Julier, Jeffrey K. Uhlmann , 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract - Cited by 778 (6 self) - Add to MetaCart
The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF

Optimal robust mean-variance hedging in incomplete financial markets

by N. Lazrieva, T. Toronjadze - Journal of Mathematical Sciences
"... Abstract. Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V-robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial mar ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Abstract. Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V-robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial

Mean–variance optimal adaptive execution

by Julian Lorenz, Robert Almgren - Applied Mathematical Finance , 2011
"... Electronic trading of equities and other securities makes heavy use of “arrival price ” algorithms, that balance the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, mean-variance optimal trading strategies are static: they do not modi ..."
Abstract - Cited by 11 (1 self) - Add to MetaCart
Electronic trading of equities and other securities makes heavy use of “arrival price ” algorithms, that balance the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, mean-variance optimal trading strategies are static: they do

A practical part-of-speech tagger

by Doug Cutting, Julian Kupiec, Jan Pedersen, Penelope Sibun - IN PROCEEDINGS OF THE THIRD CONFERENCE ON APPLIED NATURAL LANGUAGE PROCESSING , 1992
"... We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and op ..."
Abstract - Cited by 409 (5 self) - Add to MetaCart
We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies

MLESAC: A New Robust Estimator with Application to Estimating Image Geometry

by P. H. S. Torr, A. Zisserman - Computer Vision and Image Understanding , 2000
"... A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solu ..."
Abstract - Cited by 362 (10 self) - Add to MetaCart
A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative

The dynamics of reinforcement learning in cooperative multiagent systems

by Caroline Claus, Craig Boutilier - IN PROCEEDINGS OF NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-98 , 1998
"... Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that a ..."
Abstract - Cited by 377 (1 self) - Add to MetaCart
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners

Near-optimal sensor placements in gaussian processes

by Andreas Krause, Ajit Singh, Carlos Guestrin, Chris Williams - In ICML , 2005
"... When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in t ..."
Abstract - Cited by 342 (34 self) - Add to MetaCart
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance

MEAN-VARIANCE PORTFOLIO OPTIMIZATION

by Emmanuel Asumeng-denteh, Dr. Domokos Vermes, Major Advisor, Emmanuel Asumeng-denteh , 2004
"... Transaction costs and resampling are two important issues that need great attention in every portfolio investment planning. In practice costs are incurred to rebalance a portfolio. Every investor tries to find a way of avoiding high transaction cost as much as possible. In this thesis, we investigat ..."
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investigated how transaction costs and resampling affect portfolio investment. We modified the basic mean-variance optimization problem to include rebalancing costs we incur on transacting securities in the portfolio. We also reduce trading as much as possible by applying the resampling approach any time we
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