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on Market Microstructure |
| By: | Li, M. Z.; Linton, O. |
| Abstract: | We introduce a new method to estimate the integrated volatility (IV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2021a) to estimate the moments of the microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the observation times are random and endogenous, and the noise process is nonstationary, autocorrelated and dependent on the efficient price. We derive the limit distribution for the proposed estimators under infill asymptotics in a general setting. Our simulation and empirical studies demonstrate the robustness, accuracy and computational efficiency of our estimators compared to several alternatives recently proposed in the literature. |
| Date: | 2021–02–24 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2115&r=all |
| By: | Colliard, Jean-Edouard; Foucault, Thierry; Hoffmann, Peter |
| Abstract: | We propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers’ aggregate inventory position differs from zero. Price dispersion increases in the size of this position. The model generates new predictions about the effects of dealers' connectedness and dealers' aggregate inventories on prices. JEL Classification: G10, G12, G19 |
| Keywords: | interdealer trading, inventory management, OTC markets |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212529&r=all |
| By: | Donggyu Kim; Yazhen Wang |
| Abstract: | Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this paper, to account for whole-day market dynamics, we propose an overnight volatility model based on It\^o diffusions to accommodate two different instantaneous volatility processes for the open-to-close and close-to-open periods. We develop a weighted least squares method to estimate model parameters for two different periods and investigate its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and method. Finally, we apply the proposed approaches to real trading data. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.13467&r=all |
| By: | Dohyun Chun; Donggyu Kim |
| Abstract: | Recently, to account for low-frequency market dynamics, several volatility models, employing high-frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous structure. In this paper, to study state heterogeneous market dynamics based on high-frequency data, we introduce a novel volatility model based on a continuous Ito diffusion process whose intraday instantaneous volatility process evolves depending on the exogenous state variable, as well as its integrated volatility. We call it the state heterogeneous GARCH-Ito (SG-Ito) model. We suggest a quasi-likelihood estimation procedure with the realized volatility proxy and establish its asymptotic behaviors. Moreover, to test the low-frequency state heterogeneity, we develop a Wald test-type hypothesis testing procedure. The results of empirical studies suggest the existence of leverage, investor attention, market illiquidity, stock market comovement, and post-holiday effect in S&P 500 index volatility. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.13404&r=all |
| By: | Yan Peng (School of Economics and Management, Wuhan University); Jason Shachat (Durham University Business School; Economics and Management School, Wuhan University; Chapman University); Lijia Wei (School of Economics and Management, Wuhan University); S. Sarah Zhang (Alliance Manchester Business School, University of Manchester) |
| Abstract: | Using laboratory experiments, we illustrate that trading algorithms that prioritize low latency pose certain pitfalls in a variety of market structures and configurations. In hybrid double auctions markets with human traders and trading agents, we find superior performance of trading agents to human traders in balanced markets with the same number of human and Zero Intelligence Plus (ZIP) buyers and sellers only, thus providing a partial replication of Das et al. (2001). However, in unbalanced markets and extreme market structures, such as monopolies and duopolies, fast ZIP agents fall into a speed trap and both human participants and slow ZIP agents outperform fast ZIP agents. For human traders, faster reaction time significantly improves trading performance, while Theory of Mind can be detrimental for human buyers, but beneficial for human sellers. |
| Keywords: | Trading agents, Speed, Algorithmic trading, Laboratory experiment |
| JEL: | C78 C92 D40 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:20-39&r=all |