Project highlights
In algorithmic trading, researchers have often described trading as clustered or 'bursty' yet few have defined burstiness. In this research we propose to measure how trades are conducted in three ways, namely Poisson, Batch-modulated Poisson, and Markov-Modulated Poisson. An understanding of how and when to trade is critical to uncover of the price formation process, and will have many important applications from risk management to algorithmic trading. We will also examine how the seasonality of the trading day might be incorporated into this models.
The degree to which long memory is involved in the formation of price return, particularly pertaining to Asian equities, is important, for it may allow us to trade stocks with long memory differently from those without. We propose to quantify, for the Nikkei, a measure of the long memory of the time series in question: specifically, the Hurst Exponent. We will also investigate the degree to which long memory is related to other microstructural factors, primarily bid-offer spread.
Power-law models are important in high frequency models, in particular when it comes to the modeling of trade data. We propose, using Asian trade data, to analyze the impact of trading using these models by taking into account the scale of imbalance on the orderbook. We will test the applicability of our results by incorporating the model in a trading simulation.