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Horvath, Blanka Nora; Teichmann, Josef; Zuric, Zan - 2021

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those....

Persistent link: https://www.econbiz.de/10012800441

A deep learning approach to estimate forward default intensities

Divernois, Marc-Aurèle - 2020 - Preliminary draft

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Persistent link: https://www.econbiz.de/10012419329

Spanning tests for Markowitz stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2018 - Current draft: January 2018

Using properties of the cdf of a random variable defined as a saddle-type point of a real valued continuous stochastic process, we derive first-order asymptotic properties of tests for stochastic spanning w.r.t. a stochastic dominance relation. First, we define the concept of Markowitz...

Persistent link: https://www.econbiz.de/10011877232

Deep hedging: hedging derivatives under generic market frictions using reinforcement learning

Buehler, Hans; Gonon, Lukas; Teichmann, Josef; Wood, Ben; … - 2019

This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work...

Persistent link: https://www.econbiz.de/10012179635

Deep learning, jumps, and volatility bursts

Bashchenko, Oksana; Marchal, Alexis - 2020

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps...

Persistent link: https://www.econbiz.de/10012181300

Spanning analysis of stock market anomalies under prospect stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2020 - This version: April 2020

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and...

Persistent link: https://www.econbiz.de/10012219063

Quantifying reflexivity in financial markets : towards a prediction of flash crashes

Filimonov, Vladimir; Sornette, Didier - 2012

exogenous influences with self-excited dynamics, to the E-mini S&P 500 futures contracts traded in the Chicago Mercantile …% in 1998 to less than 30% since 2007 of the price changes resulting from some revealed exogenous information. Analogous to …

Persistent link: https://www.econbiz.de/10009561617

Realizing smiles: pricing options with realized volatility

Corsi, Fulvio; Fusari, Nicola; Vecchia, Davide la - 2010

We develop a discrete-time stochastic volatility option pricing model, which exploits the information contained in high …-frequency data. The Realized Volatility (RV) is used as a proxy of the unobservable log-returns volatility. We model its dynamics by … persistence and, on the other hand, provides the necessary smoothing of the noise present in the RV dynamics …

Persistent link: https://www.econbiz.de/10003973052

Conditional density models for asset pricing

Filipović, Damir; Hughston, Lane P.; Macrina, Andrea - 2010

We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional … by Brownian motion, an associated "master equation" for the dynamics of the conditional probability density is derived …

Persistent link: https://www.econbiz.de/10008797695

Pricing American options under stochastic volatility and stochastic interest rates

Medvedev, Alexey (contributor); … - 2007

We introduce a new analytical approach to price American options. Using an explicit and intuitive proxy for the exercise rule, we derive tractable pricing formulas using a short-maturity asymptotic expansion. Depending on model parameters, this method can accurately price options with...

Persistent link: https://www.econbiz.de/10003550657