112 citations · 479 across the 30 of their papers we have counts for
35 papers
Diverse Approaches to Optimal Execution Schedule Generation
Robert de Witt, Mikko S. Pakkanen
We present the first application of MAP-Elites, a quality-diversity algorithm, to trade execution. Rather than searching for a single optimal policy, MAP-Elites generates a diverse…
Wasserstein error estimates between telegraph processes and Brownian motion
Gerardo Barrera, Jani Lukkarinen, Mikko S. Pakkanen
We provide non-asymptotic error bounds in the path Wasserstein distance with quadratic integral cost between suitable functionals of the telegraph process and the corresponding fun…
Learning with Expected Signatures: Theory and Applications
Lorenzo Lucchese, Mikko S. Pakkanen, Almut E. D. Veraart
The expected signature maps a collection of data streams to a lower dimensional representation, with a remarkable property: the resulting feature tensor can fully characterize the…
Estimation and Inference for Multivariate Continuous-time Autoregressive Processes
Lorenzo Lucchese, Mikko S. Pakkanen, Almut E. D. Veraart
The aim of this paper is to develop estimation and inference methods for the drift parameters of multivariate Lévy-driven continuous-time autoregressive processes of order $p\in\ma…
The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective
Lorenzo Lucchese, Mikko Pakkanen, Almut Veraart
In this paper, we conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns by leveraging deep learning techniques. First, we introduc…
Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty
Matthew J. Penn, Daniel J. Laydon, Joseph Penn +7
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease r…
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions
Phillip Murray, Ben Wood, Hans Buehler +2
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-aver…
Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures
Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1
We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e.g. for a spot price and options written…
Limit theorems for the realised semicovariances of multivariate Brownian semistationary processes
Yuan Li, Mikko S. Pakkanen, Almut E. D. Veraart
In this article we will introduce the realised semicovariance for Brownian semistationary (BSS) processes, which is obtained from the decomposition of the realised covariance matri…
Non-average price impact in order-driven markets
Claudio Bellani, Damiano Brigo, Mikko Pakkanen +1
We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios. Given the order book data associated with one single…
Unifying incidence and prevalence under a time-varying general branching process
Mikko S. Pakkanen, Xenia Miscouridou, Matthew J. Penn +5
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a ti…
Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics
Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1
We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs…
A GMM approach to estimate the roughness of stochastic volatility
Anine E. Bolko, Kim Christensen, Mikko S. Pakkanen +1
We develop a GMM approach for estimation of log-normal stochastic volatility models driven by a fractional Brownian motion with unrestricted Hurst exponent. We show that a paramete…
Limit Theorems for Trawl Processes
Mikko S. Pakkanen, Riccardo Passeggeri, Orimar Sauri +1
In this work we derive limit theorems for trawl processes. First,we study the asymptotic behaviour of the partial sums of the discretized trawl process $(X_{iΔ_{n}})_{i=0}^{\lfloor…
Feasible Inference for Stochastic Volatility in Brownian Semistationary Processes
Phillip Murray, Riccardo Passeggeri, Almut E. D. Veraart +1
This article studies the finite sample behaviour of a number of estimators for the integrated power volatility process of a Brownian semistationary process in the non semi-martinga…
State-dependent Hawkes processes and their application to limit order book modelling
Maxime Morariu-Patrichi, Mikko S. Pakkanen
We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process a…
Hybrid simulation scheme for volatility modulated moving average fields
Claudio Heinrich, Mikko S. Pakkanen, Almut E. D. Veraart
We develop a simulation scheme for a class of spatial stochastic processes called volatility modulated moving averages. A characteristic feature of this model is that the behaviour…
Turbocharging Monte Carlo pricing for the rough Bergomi model
Ryan McCrickerd, Mikko S. Pakkanen
The rough Bergomi model, introduced by Bayer, Friz and Gatheral [Quant. Finance 16(6), 887-904, 2016], is one of the recent rough volatility models that are consistent with the sty…
Hybrid marked point processes: characterisation, existence and uniqueness
Maxime Morariu-Patrichi, Mikko S. Pakkanen
We introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such…
Pathwise large deviations for the Rough Bergomi model
Antoine Jacquier, Mikko S. Pakkanen, Henry Stone
We study the small-time behaviour of the rough Bergomi model, introduced by Bayer, Friz and Gatheral (2016), and prove a large deviations principle for a rescaled version of the no…
Decoupling the short- and long-term behavior of stochastic volatility
Mikkel Bennedsen, Asger Lunde, Mikko S. Pakkanen
We introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrela…
The Local Fractional Bootstrap
Mikkel Bennedsen, Ulrich Hounyo, Asger Lunde +1
We introduce a bootstrap procedure for high-frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample p…
Arbitrage without borrowing or short selling?
Jani Lukkarinen, Mikko S. Pakkanen
We show that a trader, who starts with no initial wealth and is not allowed to borrow money or short sell assets, is theoretically able to attain positive wealth by continuous trad…
On the conditional small ball property of multivariate Lévy-driven moving average processes
Mikko S. Pakkanen, Tommi Sottinen, Adil Yazigi
We study whether a multivariate Lévy-driven moving average process can shadow arbitrarily closely any continuous path, starting from the present value of the process, with positive…