No Tick-Size Too Small: A General Method for Modelling Small Tick Limit Order Books
Tick sizes not only influence the granularity of the price formation process but also affect market agents' behavior. We investigate the disparity in the microstructural properties of the Limit Order Book (LOB) across different relative tick sizes. A key contribution of this study is the identification of several stylized facts, which are used to differentiate between large, medium, and small tick stocks, along with clear metrics for their measurement. We provide cross-asset visualizations to illustrate how these attributes vary with relative tick size. Further, we propose a Hawkes Process model that accounts for sparsity, multi-tick level price moves, and the shape of the book in small-tick stocks. Through simulation studies, we demonstrate the universality of the model and identify key variables that determine whether a simulated LOB resembles a large-tick or small-tick stock. Our tests show that stylized facts like sparsity, shape, and relative returns distribution can be smoothly transitioned from a large-tick to a small-tick asset using our model. We test this model's assumptions, showcase its challenges and propose questions for further directions in this area of research.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On bid and ask side-specific tick sizes
The tick size, which is the smallest increment between two consecutive prices for a given asset, is a key parameter of market microstructure. In particular, the behavior of high frequency market makers is highly related …
ParkingSticker: A Real-World Object Detection Dataset
We present a new and challenging object detection dataset, ParkingSticker, which mimics the type of data available in industry problems more closely than popular existing datasets like PASCAL VOC. ParkingSticker contains…
Objectobject-detectionObject DetectionSuper Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model. In thi…
Model CompressionMulti-Task LearningOne ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these "winning …
AllNot All Lotteries Are Made Equal
The Lottery Ticket Hypothesis (LTH) states that for a reasonably sized neural network, there exists a subnetwork within the same network that yields no less performance than the dense counterpart when trained from the sa…
All