We develop an adaptive learning game to rethink efficient markets. We use the stochastically stable state of this game, which is a mixed Nash equilibrium, to form an adaptive expectation model that provides an estimate of the confidence interval for prices on the next day. The estimate is most...

This study provides an overview of the model evolution and research trends in the field of financial and risk modelling by applying a bibliometric approach from 2008–2019 and an overall citation network analysis. We present a content analysis of contributing authors, countries, journals, main...

While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps...

Jumps and cojumps are examined in the covariance matrices of high-frequency financial markets. We propose a new method for identifying intraday volatility jumps in the diffusive covariance matrix of asset pairs. Our method avoids model misspecification errors, is able to identify multiple...

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: i) The price impact of liquidity trades is higher in the...

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: i) The price impact of liquidity trades is higher in the...

Using the staggered entry of Chi-X in 12 European equity markets as a source of exogenous variation in high frequency trading (HFT), we find that HFT causes significant increases in co-movement in returns and in liquidity. About one-third of the increase in return co-movement is due to faster...

Algorithmic trading is generally defined as using computer-generated algorithms to create and execute orders on marketplaces. Recently, such algo-trading strategies are increasingly being associated with the negative impact on capital markets – both from a technological as well as a business...

High-frequency trading (HFT) has been dominating the activity in developed financial markets in the last two decades. Despite its recent formation, the literature on the impacts of HFT on financial markets and participants is broad. However, there are ongoing debates and unanswered questions...

The current research assesses the risks commonly attributed to the presence of HFT in the context of different market structures deployed by the U.S. exchanges. In particular, we find that, by design, the so-called “normal” exchanges have the lowest market quality, including the highest...