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on Market Microstructure |
| By: | Vassilis Polimenis |
| Abstract: | Informed traders need to trade fast in order to profit from their private information before it becomes public. Fast electronic markets provide such liquidity. Slow markets provide execution in an auction based trading floor. Hybrid markets combine both execution venues. In its main result, the paper shows that to compensate for their slow and risky executions, trading floors need to be at least twice as deep as the sweeping facility. Furthermore, when a stand-alone trading floor is enhanced with the addition of a sweeping facility, overall informed trading will decline because it is easier for informed traders to extract the full value of their private info. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.06445&r=all |
| By: | Worapree Maneesoonthorn; Gael M. Martin; Catherine S. Forbes |
| Abstract: | We conduct an extensive evaluation of price jump tests based on high-frequency financial data. After providing a concise review of multiple alternative tests, we document the size and power of all tests in a range of empirically relevant scenarios. Particular focus is given to the robustness of test performance to the presence of jumps in volatility and microstructure noise, and to the impact of sampling frequency. The paper concludes by providing guidelines for empirical researchers about which test to choose in any given setting. |
| Keywords: | price jump tests, nonparametric jump measures, bivariate jump diffusion model, volatility jumps, microstructure noise, sampling frequency. |
| JEL: | C12 C22 C58 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-3&r=all |
| By: | Boyue Fang; Yutong Feng |
| Abstract: | Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading ($VPIN$), Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market-making transactions, uncovering the relationship between discrete dimensional data from the projection of high-dimensional time-series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return. |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1912.10343&r=all |
| By: | Sabiou M. Inoua (Chapman University) |
| Abstract: | Financial volatility obeys two well-established empirical properties: it is fattailed (power-law distributed) and it tends to be clustered in time. Many interesting models have been proposed to account for these regularities, notably agent-based computational models, which typically invoke complicated mechanisms, however. It can be shown that trend-following speculation generates the power law in an intrinsic way. But this model cannot exaplain clustered volatility. This paper extends the model and offers a simple explanation for clustered volatility: the impact of exogenous news on traders’ expectations. Owing to the famous no-trade results, rational expectations, the dominant model of news-driven expectations, is hard to reconcile with the incessant high-frequency trading behind the volatility clustering. The simplest alternative model of news-driven expectations is to assume that traders have prior views about the market (an asset’s future price change or its present value) and then modify their views with the advent of a news. This simple news-driven random walk of traders’ expectations explains volatility clustering in a generic way. Liquidity plays a crucial role in this dynamics of volatility, which is emphasized in a dicussions section. |
| Keywords: | Volatility Clustering; Power Law; Trend Following; Efficient Market Hypothesis; Liquidity |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:19-33&r=all |