This thesis focuses on the training deep learning models for the prediction of the present values as well as the sensitivities (derivatives) with respect to the
Abstract Deep learning exotic derivatives Gunnlaugur Geirsson Monte Carlo methods in derivative pricing are computationally expensive, in
Deep hedging is an unsupervised learning-based approach to determine optimal hedging strate- gies for options and other derivatives It was originally devised
Last time: key ingredients of deep learning success Algorithms Compute Review from calculus: derivatives and gradients
Exotic Derivatives and Deep Learning AXEL BROSTRÖM RICHARD KRISTIANSSON Degree Projects in Financial Mathematics (30 ECTS credits)
Main limits of the deep hedging algorithm 09 Conclusion 09 References Deep hedging: application of deep learning to hedge financial derivatives
Why consider Deep Neural Networks for Financial Derivatives? In the past decades: There has been a significant growth in the use of financial derivatives
Exotic Derivatives and Deep Learning AXEL BROSTRÖM RICHARD KRISTIANSSON Degree Projects in Financial Mathematics (30 ECTS credits)
Why consider Deep Neural Networks for Financial Derivatives? In the past decades: There has been a significant growth in the use of financial derivatives
Confusion can arise because AD does in fact provide numerical values of derivatives (as opposed to derivative expressions) and it does so by using symbolic