Abstract
Given recent requirements for ensuring the robustness of algorithmic trading strategies laid out in the Markets in Financial Instruments Directive II, this paper proposes a novel agent-based simulation for exploring algorithmic trading strategies. Five different types of agents are present in the market. The statistical properties of the simulated market are compared with equity market depth data from the Chi-X exchange and found to be significantly similar. The model is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The results are found to be insensitive to reasonable parameter variations.
Highlights
Over the last three decades, there has been a significant change in the financial trading ecosystem
We begin by performing a global sensitivity analysis to explore the influence of the parameters on market dynamics and ensure the robustness of the model
We explore the existence of the following stylised facts in depth-of-book data from the Chi-X exchange compared with our model: fat tailed distribution of returns, volatility clustering, autocorrelation of returns, long memory in order flow, concave price impact function and the existence of extreme price events
Summary
Over the last three decades, there has been a significant change in the financial trading ecosystem. Since its introduction, recurring periods of high volatility and extreme stock price behaviour have plagued the markets. This paper presents a model that represents a richer set of trading behaviours and is able to replicate more of the empirically observed empirical regularities than any other paper. Such abilities provide a crucial step towards a viable platform for the testing of trading algorithms as outlined in MiFID II. We find that, in certain proportions, the presence of high-frequency trading agents gives rise to the occurrence of extreme price events.
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