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Signal extractions with applications in finance

Abstract : The main objective of this PhD dissertation is to set up new signal extraction techniques with applications in Finance. In our setting, a signal is defined in two ways. In the framework of investement strategies, a signal is a function which generates buy/sell orders. In denoising theory, a signal, is a function disrupted by some noise, that we want to recover. A first part of this PhD studies historical volatility spillovers around corporate earning announcements. Notably, we study whether a move by one point in the announcer historical volatility in time t will generate a move by beta percents in time t+1. We find evidences of volatility spillovers and we study their intensity across variables such as : the announcement outcome, the surprise effect, the announcer capitalization, the market sentiment regarding the announcer, and other variables. We illustrate our finding by a volatility arbitrage strategy. The second part of the dissertation adapts denoising techniques coming from imagery : wavelets and total variation methods, to forms of noise observed in finance. A first paper proposes an denoising algorithm for a signal disrupted by a noise with a spatially varying standard-deviation. A financial application to volatility modelling is proposed. A second paper adapts the Bayesian representation of the Rudin, Osher and Fatemi approach to asymmetric and leptokurtic noises. A financial application is proposed to the denoising of intra-day stock prices in order to implement a pattern recognition trading strategy.
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Submitted on : Friday, November 23, 2018 - 9:37:06 AM
Last modification on : Friday, June 19, 2020 - 2:51:12 PM


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  • HAL Id : tel-01932475, version 1



Clément Goulet. Signal extractions with applications in finance. General Finance [q-fin.GN]. Université Panthéon-Sorbonne - Paris I, 2017. English. ⟨NNT : 2017PA01E066⟩. ⟨tel-01932475⟩



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