FX market volatility modelling: Can we use low-frequency data?

Modelling the High-Frequency FX Market: an Agent-Based Approach

In this paper, we use an agent-based approach to model the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We focus on performing a systematic exploration of the market’s constituent elements and their impact on the dynamics of the market behaviour. In particular, our study explores and identifies the essential elements under which th...

Modeling High-Frequency FX Data Dynamics∗

This paper shows that high-frequency, irregularly-spaced, FX data can generate non-normality, conditional heteroskedasticity, and leptokurtosis when aggregated into fixed-interval, calendar time even when these features are absent in the original D.G.P. Furthermore, we introduce a new approach to modeling these high-frequency irregularly spaced data based on the Poisson regression model. The ne...

Modeling the High-Frequency FX Market: An Agent-Based Approach

The development of computational-intelligence based strategies for electronic markets has been the focus of intense research. In order to be able to design efficient and effective automated trading strategies, one first needs to understand the workings of the market, the strategies that traders use and their interactions as well as the patterns emerging as a result of these interactions. In thi...

FX Smile Modelling

Nonlinear Features of Realized Fx Volatility

This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to estimate ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAXmodel. We explore nonlinear departures from these lin...