Advances in variance analysis permit the splitting of the total quadratic variation of a jump-diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of...

This paper develops a new neural network architecture for modeling spatial distributions (i.e., distributions on R^d) which is computationally efficient. The design of the architecture takes advantage of the specific structure of limit order books. The new architecture, which we refer to as a...

We develop a volatility estimator that can be directly applied to tick-by-tick data. More specifically, we consider a model that allows for (i) irregular observation times that can be endogenous, (ii) dependent noise that can have diurnal features and be dependent on the latent price process,...

We use an economic experiment to examine the impact of an uncertain level of asymmetric information on the behavior of security dealers. Specifically, we distinguish three types of uncertainty with respect to informed trading - risk, compound risk, and ambiguity - for both a monopoly and a...

We investigate the effects of introducing a fee on excessive order to trade ratios (OTR) on market quality at the Oslo Stock Exchange (OSE). We find that traders reacted to the regulation, as measured OTRs fell. However, market quality, measured with depth, spreads, and realized volatility,...

Using data from the first phase implementation of the Toronto Stock Exchange (TSX) Long life order type on November 30, 2015, we estimate market quality, execution quality and price discovery metrics in order to investigate the impact of the new order type on 26 eligible stocks listed on the...

In this dissertation a simple model for limit order books is formulated. Assuming a discrete time model and an arrival process independent of each state, the limit order book is simulated with different distribution for prices, including uniform distribution and binomial distribution, and change...

Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and sequence classification. They have been applied extensively to forecasting univariate financial time series, however their application to high frequency trading has not been...

Using an extensive, time-series, cross-sectional data-set of actively traded Indian stocks with up to 1.75 million firm-day observations, we discern the key determinants of commonality in liquidity among emerging markets.The paper shows evidence for both supply-side and demand-side factors...