Probability of informed trading during the COVID‑19 pandemic:the case of the Romanian stock market

Using data from the Bucharest Stock Exchange,we examine the factors influencing the probability of informed trading(PIN)during February—October 2020,a COVID-19 pandemic period.Based on an unconditional quantile regression approach,we show that PIN exhibit asymmetric dependency with liquidity and trading costs.Furthermore,building a customized database that contains all insider transactions on the Bucharest Stock Exchange,we reveal that these types of orders monotonically increase the infor-mation asymmetry from the 50th to the 90th quantile throughout the PIN distribution.Finally,we bring strong empirical evidence associating the level of information asym-metry to the level of fake news related to the COVID-19 pandemic.This novel result suggests that during episodes when the level of PIN is medium to high(between 15 and 50%),any COVID-19 related news classified as misinformation released during the lockdown period,is discouraging informed traders to place buy or sell orders condi-tioned by their private information.

The Quadratic-Form Representation of the Pre-Averaging Estimator

Volatility forecasts are central to many financial issues, including empirical asset pricing finance and risk management. In this paper, I derive a new quadratic-form representation of the pre-averaging volatility estimator of Jacod et al. （2009）, which allows for the theoretical analysis of its forecasting performance.

Dependencies between price duration, volatility, volume and return on the Warsaw Stock Exchange

The successive changes of asset prices are the most visible manifestation of financial markets dynamics. There exist different views about factors generating these changes, but many researchers and practitioners agree that the most important among them is the impact of information flow. According to the market microstructure theories, it depends mainly on the behavior of informed and uniformed traders. In the paper, we investigate dependencies between the possible proxies of information process： price duration and corresponding to it volume change and return. Our main objective is to answer the question about the most important factor in the process of discovering information by uniformed traders. We apply a set of models for volatility, volume and duration data. Our analysis is performed for selected equities listed on the Warsaw Stock Exchange and uses tick-by-tick data. The obtained results show that the stock liquidity on this leading stock market in Central and Eastern Europe is the most important factor influencing the process of discovering information by uninformed traders.

Study on the Intraday Pattern and the Dynamic Correlation Among Return,Volume and Open Interest——Evidence from Chinese Commodity Futures Markets

This paper uses minute by minute data series from Chinese commodity futures markets to study patterns of intraday effect and discovers the L pattern of absolute return and volume.It is different from stock market,which has a distinctive pattern of U-shaped.The financial market microstructure theory,traders' psychology and trading mechanism are applied to explain it.Then this paper studies the factors that influence volatility of return and the lagged orders.The results show that there is a bilateral Granger causality among any two of the absolute return,volume and open interest,and it is different from the empirical results of the stock market,in the sense that there is only a unilateral Granger causal relationship from volume to absolute return.The authors also analyze the dynamic relationship among these three factors.The empirical results tell that the influence of open interest on volatility of absolute return and volume is weak,and there is a strong correlation between absolute return and volume.Some investment suggestions are offered from the analysis mentioned above.

Analysis of Trading Volume Behavior Under Continuous Double Auction Mechanism in Limit Order Markets

In this paper, we develop a theoretical model to describe the dynamics of the trading volume under continuous double auction mechanism in limit order markets. We examine the formation process and statistical properties （including the mean, wriance, and realized value） of the buy side cumulative trading volume, sell side cumulative trading volume and total cumulative volume under continuous double auction mechanism by means of mathematical modeling based on Poisson process of order flows, and do some corresponding numerical simulations and comparative statics on the factors that would influence these three volumes aforementioned. The results indicate that these three volumes are all influenced by the factors including the arrival rate of orders, demands of each order, proportional structure between buy and sell orders, executed probability and time interval we examined. And our established theoretical model can well capture the dynamics of these three volumes under continuous double auction mechanism in limit order markets when all these factors interact.

High-Dimensional Volatility Matrix Estimation with Cross-Sectional Dependent and Heavy-Tailed Microstructural Noise

The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications.However,most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise,which are typically violated in the financial markets.In this paper,the authors proposed a new robust volatility matrix estimator,with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises,and demonstrated that it can achieve the optimal convergence rate n-1/4.Furthermore,the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components,using an appropriate regularization procedure.Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise.Additionally,an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.

A QUANTITATIVE MODEL FOR INTRADAY STOCK PRICE CHANGES BASED ON ORDER FLOWS

This paper proposes a double Markov model of the double continuous auction for describing intra-day price changes. The model splits intra-day price changes as the repetition of one tick price moves and assumes order arrivals are independent Poisson random processes. The dynamic process of price formation is described by a birth-death process of the double M/M/1 server queue corresponding to the best bid/ask. The initial depths of the best bid and ask are defined as different constants depending on the last price change. Thus, the price changes in the model follow a first-order Markov process. As the initial depth of the best bid/ask is originally larger than that of the opposite side when the last price is down/up, the model may explain the negative autocorrelations of the price of the best bid/ask. The estimated parameters are based on the real tick-by-tick data of the Nikkei 225 futures listed in Osaka Stock Exchanges. The authors find the model accurately predicts the returns of Osaka Stock Exchange average.

Analysis of High Frequency Data in Finance:A Survey

This study examines the use of high frequency data in finance,including volatility estimation and jump tests.High frequency data allows the construction of model-free volatility measures for asset returns.Realized variance is a consistent estimator of quadratic variation under mild regularity conditions.Other variation concepts,such as power variation and bipower variation,are useful and important for analyzing high frequency data when jumps are present.High frequency data can also be used to test jumps in asset prices.We discuss three jump tests:bipower variation test,power variation test,and variance swap test in this study.The presence of market microstructure noise complicates the analysis of high frequency data.The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise.Finally,some applications of jump tests in asset pricing are discussed in this article.