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on Market Microstructure |
| By: | Fu, Servanna Mianjun; Kellard, Neil; Verousis, Thanos; Kalaitzoglou, Iordanis |
| Abstract: | Using Trade and Quote (TAQ) data to infer variation in High frequency Trading (HFT) for the US equity markets and HFT start and colocation dates for a sample of 10 international exchanges, we find that increases in HFT activity lead to a significant increase in stock herding. The effect of HFT on herding is more pronounced for large-cap stocks, higher liquidity periods and during more volatile days. HFT activities are strongly associated with non-fundamental herding and encourage information cascades that induce price inefficiencies, suggesting changes to market design might be warranted. |
| Keywords: | High Frequency Trading; HFT; Herding; Colocation; Information cascades; Fundamental information |
| Date: | 2024–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:37485&r=mst |
| By: | Gian Piero Aielli (University of Bologna, Dept. Mathematics); Davide Pirino (CEIS & DEF, University of Rome "Tor Vergata") |
| Abstract: | In accordance with trade signals that operate in the market, we design a microfounded structural model of price formation that features partially informed and noise traders. The former only have information on whether a trend in the latent price dynamic is underway. Without any trend, the partially informed agents do not trade, and prices do not update unless a noise agent activates. Assuming market efficiency, we impose zero expected net profit per trade. With dedicated parametric assumptions, we analytically derive the model’s likelihood, which allows reliable daily estimates (exclusively based on intra-day transaction prices) of the stocks’ market liquidities and funding liquidity (and their estimation errors). Theory predicates that stocks’ volatilities, stocks’ market liquidities, and funding liquiditymay interact in a non-trivial fashion. To shed light on their nature and mutual influence, we model their dynamics through an MGARCH-VAR process. The model is flexible enough to capture some of the well-known empirical features of financial data, such as fat-tailed distributions and conditional heteroskedasticity. Following an econometric methodology of standard practice in the realized volatility literature, the model is fitted on estimates (obtained fromintra-day data through the structural model estimation) of the daily proxies for stocks’ volatilities, stocks’ market liquidities, and funding liquidity. On a dataset of NYSE stocks, we find significant evidence in favor of four stylized facts: (i) stocks’ volatilities, stocks’ market liquidities, and funding liquidity co-move; (ii) co-movements are stronger when funding liquidity dries up; (iii) stocks with lower volatility are characterized by higher market liquidity, and (iv) funding liquidity restrictions have a stronger impact on stocks’ market illiquidities of high-volatility stocks. |
| Keywords: | funding illiquidity, market illiquidity, structural estimation, marketmicrostructure. |
| Date: | 2023–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:568&r=mst |
| By: | Konark Jain; Nick Firoozye; Jonathan Kochems; Philip Treleaven |
| Abstract: | Hawkes Process has been used to model Limit Order Book (LOB) dynamics in several ways in the literature however the focus has been limited to capturing the inter-event times while the order size is usually assumed to be constant. We propose a novel methodology of using Compound Hawkes Process for the LOB where each event has an order size sampled from a calibrated distribution. The process is formulated in a novel way such that the spread of the process always remains positive. Further, we condition the model parameters on time of day to support empirical observations. We make use of an enhanced non-parametric method to calibrate the Hawkes kernels and allow for inhibitory cross-excitation kernels. We showcase the results and quality of fits for an equity stock's LOB in the NASDAQ exchange. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.08927&r=mst |
| By: | Aaron Wheeler; Jeffrey D. Varner |
| Abstract: | In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the number and complexity of simulated agents. Heterogeneous agents make decisions in parallel, and their orders are processed through a realistic, continuous double auction matching engine. We present a baseline model implementation and show that it captures several known statistical properties of real financial markets (i.e., stylized facts). Further, we demonstrate these results without fitting models to historical financial data. Thus, this framework could be used for direct applications such as human-in-the-loop machine learning or to explore theoretically exciting questions about market microstructure's role in forming the statistical regularities of real markets. To the best of our knowledge, this study is the first to implement multiple assets, parallel agent decision-making, a continuous double auction mechanism, and intelligent agent types in a scalable real-time environment. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.14903&r=mst |
| By: | Ulrich Horst; Wei Xu; Rouyi Zhang |
| Abstract: | We establish the weak convergence of the intensity of a nearly-unstable Hawkes process with heavy-tailed kernel. Our result is used to derive a scaling limit for a financial market model where orders to buy or sell an asset arrive according to a Hawkes process with power-law kernel. After suitable rescaling the price-volatility process converges weakly to a rough Heston model. Our convergence result is much stronger than previously established ones that have either focused on light-tailed kernels or the convergence of integrated volatility process. The key is to establish the tightness of the family of rescaled volatility processes. This is achieved by introducing a new methods to establish the $C$-tightness of c\`adl\`ag processes based on the classical Kolmogorov-Chentsov tightness criterion for continuous processes. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.08784&r=mst |