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Gianluca Fusai

Researcher at City University London

Publications -  76
Citations -  1514

Gianluca Fusai is an academic researcher from City University London. The author has contributed to research in topics: Asian option & Valuation of options. The author has an hindex of 19, co-authored 72 publications receiving 1331 citations. Previous affiliations of Gianluca Fusai include University of London & University of Warwick.

Papers
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Functional clustering and linear regression for peak load forecasting

TL;DR: This paper takes advantage of the functional nature of the data-set and proposes a forecasting methodology based on functional statistics, using a functional clustering procedure to classify the daily load curves and defines a family of functional linear regression models.
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Pricing discretely monitored Asian options under Levy processes

TL;DR: In this paper, the authors present methodologies to price discretely monitored Asian options when the underlying evolves according to a generic Levy process, and compare the implementation of their method to Monte Carlo simulation implemented with control variates and using different parametric Levy processes.
Book

Implementing Models in Quantitative Finance: Methods and Cases

TL;DR: In this article, the Laplace Transform is used to construct a dependency structure using Copula functions and a smiling GARCH is used for portfolio selection in a k-bit trading system.
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An exact analytical solution for discrete barrier options

TL;DR: An analytical solution for pricing discrete barrier options in the Black-Scholes framework is provided by reducing the valuation problem to a Wiener-Hopf equation that can be solved analytically.
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Spitzer identity, Wiener-Hopf factorization and pricing of discretely monitored exotic options

TL;DR: This work proposes a constructive procedure for the computation of the Wiener-Hopf factors, valid for both single and double barriers, based on the combined use of the Hilbert and the z-transform, and shows that the computational cost is independent of the number of monitoring dates and the error decays exponentially with thenumber of grid points.