Monte Carlo Simulation with Machine Learning for Pricing American Options and Convertible Bonds

2015 ◽  
Author(s):  
Bella Dubrov
Keyword(s):  
Machine Learning ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
American Options ◽  
Convertible Bonds

scholarly journals 3D convolutional GAN for fast simulation

2019 ◽  
Vol 214 ◽  
pp. 02010 ◽  
Author(s):  
Sofia Vallecorsa ◽  
Federico Carminati ◽  
Gulrukh Khattak
Keyword(s):  
Machine Learning ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Fast Simulation ◽  
Geant4 Monte Carlo Simulation ◽  
Detector Simulation

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach.

Simulate, Optimise, Partition: Algorithmic Diagrams of Pattern Recognition from 1953 Onwards

2016 ◽  
Author(s):  
Adrian Mackenzie
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Deep Learning ◽  
Clustering Algorithms ◽  
Flow Through ◽  
The Cold War ◽  
Hand Writing

Contemporary attempts to find patterns in data, ranging from the now mundane technologies of hand-writing recognition through to mammoth infrastructure-heavy practices of deep learning conducted by major business and government actors, rely on a group of techniques intensively developed during the 1950-60s in physics, engineering and psychology. Whether we designate them as pattern recognition, data mining, or machine learning, these techniques all seek to uncover patterns in data that cannot appear directly to the human eye, either because there are too many items for anyone to look at, or because the patterns are too subtly woven through in the data. From the techniques in current use, three developed in the Cold War era iconify contemporary modes of pattern finding: Monte Carlo simulation, gradient descent, and clustering algorithms that search for groups or clusters in data. Each of these techniques implements a different mode of pattern, and these different modes of pattern recognition flow through into contemporary scientific, technological, business and governmental problematizations. The different perspectives on event, trajectory, and proximity they embody imbue many power relations, forms of value and the play of truth/falsehood today.

A NEW MONTE CARLO METHOD FOR AMERICAN OPTIONS

2004 ◽  
Vol 07 (05) ◽  
pp. 591-614 ◽  
Author(s):  
G. N. MILSTEIN ◽  
O. REIß ◽  
J. SCHOENMAKERS
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Boundary Value Problem ◽  
Numerical Experiments ◽  
American Options ◽  
American Option ◽  
Parabolic Boundary ◽  
Black Scholes ◽  
Exercise Boundary

We introduce a new Monte Carlo method for constructing the exercise boundary of an American option in a generalized Black–Scholes framework. Based on a known exercise boundary, it is shown how to price and hedge the American option by Monte Carlo simulation of suitable probabilistic representations in connection with the respective parabolic boundary value problem. The method presented is supported by numerical experiments.

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