Extension and calibration of a Hawkes-based optimal execution model

We provide some theoretical extensions and a calibration protocol for our former dynamic optimal execution model. The Hawkes parameters and the propagator are estimated independently on financial data from stocks of the CAC40. Interestingly, the propagator exhibits a smoothly decaying form with one or two dominant time scales, but only so after a few seconds that the market needs to adjust after a large trade. Motivated by our estimation results, we derive the optimal execution strategy for a multi-exponential Hawkes kernel and backtest it on the data for round trips. We find that the strategy is profitable on average when trading at the midprice, which is in accordance with violated martingale conditions. However, in most cases, these profits vanish when we take bid-ask costs into account

A multiobjective approach using consistent rate curves to the calibration of a Gaussian Heath-Jarrow-Morton model

In this paper we propose an alternate calibration algorithm, by using a consistent family of yield curves, that fits a Gaussian Heath-Jarrow-Morton model jointly to the implied volatilities of caps and zero-coupon bond prices. The algorithm is capable for finding several Pareto optimal points as is expected for a general nonlinear multicriteria optimization problem. The calibration approach is evaluated in terms of in-sample data fitting as well as stability of parameter estimates. Furthermore, the efficiency is tested against a non-consistent traditional method by using simulated and US market data.HJM models, consistent forward rate curves, multiobjective calibration

Stochastic simulation framework for the Limit Order Book using liquidity motivated agents

In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimisation. We then demonstrate how such an agent-based modelling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios

Implied Calibration of Stochastic Volatility Jump Diffusion Models

In the context of arbitrage-free modelling of financial derivatives, we introduce a novel calibration technique for models in the affine- quadratic class for the purpose of contingent claims pricing and risk- management. In particular, we aim at calibrating a stochastic volatility jump diffusion model to the whole market volatility surface at any given time. We numerically implement the algorithm and show that the proposed approach is both stable and accurate.Affine-quadratic models, Option pricing, Model Calibration

Efficient hierarchical approximation of high-dimensional option pricing problems

A major challenge in computational finance is the pricing of options that depend on a large number of risk factors. Prominent examples are basket or index options where dozens or even hundreds of stocks constitute the underlying asset and determine the dimensionality of the corresponding degenerate parabolic equation. The objective of this article is to show how an efficient discretisation can be achieved by hierarchical approximation as well as asymptotic expansions of the underlying continuous problem. The relation to a number of state-of-the-art methods is highlighted