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...

Estimating market liquidity based on low-frequency (daily) data is important for both empirical research and investment practice. We apply machine learning to estimate market liquidity by combining human-engineered liquidity proxies based on microstructure models and widely available...

The estimation of market impact is crucial for measuring the information content of trades and for transaction cost analysis. Hasbrouck's (1991) seminal paper proposed a Structural-VAR (S-VAR) to jointly model mid-quote changes and trade signs. Recent literature has highlighted some pitfalls of...

We present a general framework for modelling the dynamics of limit order books, built on the combination of two modelling ingredients: the order flow, modelled as a general spatial point process, and market clearing, modelled via a deterministic ‘mass transport’ operator acting on...

The estimation of the PIN model and its extensions has posed significant challenges due to various computational problems. To address these issues, we propose a novel estimation method called the Expectation-Conditional Maximization (ECM) algorithm, which can serve as an alternative to existing...

This study suggests a novel approach for decomposing an options order imbalance based on trading motives using put-call parity. The options order imbalance is separated into two components: one that contains arbitrage trading and one that does not. Intuitively, the proportion of the former...