Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and...

We present an embarrassingly simple method for supervised learning of SABR model's European option price function based on lookup table or rote machine learning. Performance in time domain is comparable to generally used analytic approximations utilized in financial industry. However, unlike the...

Efficiently calibrating agent-based models (ABMs) to real data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs by combining machine-learning and intelligent iterative sampling. The proposed approach "learns" a fast surrogate meta-model...

Systematic trading is a method that is currently extremely popular in the investment world. The testing of systematic trading rules is usually done through backtesting and is at high risk of spurious accuracy as a result of the data-mining bias (DMB) present from testing multiple rules...

The proliferation of algorithmic high-frequency trading in financial markets has also led to an increase in new types of fraudulent activity. Since the flash-crash of 2010 first brought it to popular prominence, layering or spoofing fraud has become a major concern for financial regulators...

Implied volatility skew and smile are ubiquitous phenomena in the financial derivative market especially after the Black Monday 1987 crash. Various stochastic volatility models have been proposed to capture volatility skew and smile in derivative pricing and hedging. Almost 30 years after the...

Backtesting stock market investment strategies is fraught with danger – for example, overfitting. The signal to noise ratio in stock markets is so low that overfitting is inevitable. Simulation offers a means of assessing and compensating for the dangers. It is not obvious at first how...

The Polynomial Chaos Expansion (PCE) technique recovers a finite second order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochastic quantity $\xi$, hence acting as a kind of random basis. The PCE methodology has been developed...

In this paper, we propose two practicable approaches for consistently modelling the realworld and risk-neutral measures within cross-asset Monte-Carlo frameworks. We go on to explore the necessity of supporting the real-world measure and consider its calibration with the aid of an explicit...