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The two square root laws of market impact and the role of sophisticated market participants
The goal of this paper is to disentangle the roles of volume and of participation rate in the price response of the market to a sequence of transactions.
Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility.
Risk of Transfer Learning and its Applications in Finance
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Understanding the least well-kept secret of high-frequency trading
To this end, we study a market-making problem which allows us to view the imbalance as an optimal response to price moves.
Equilibria and incentives for illiquid auction markets
We study a toy two-player game for periodic double auction markets to generate liquidity.
Transfer Learning for Portfolio Optimization
In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of "transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.
Towards systematic intraday news screening: a liquidity-focused approach
Given the huge amount of news articles published each day, most of which are neutral, we present a systematic news screening method to identify the ``true'' impactful ones, aiming for more effective development of news sentiment learning methods.
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
In this paper we build for the first time, to the best of our knowledge, a mathematical framework for the general procedure of transfer learning.
Multi-asset market making under the quadratic rough Heston
Given the promising results on joint modeling of SPX/VIX smiles of the recently introduced quadratic rough Heston model, we consider a multi-asset market making problem on SPX and its derivatives, e. g. VIX futures, SPX and VIX options.
Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge.
Docent: A content-based recommendation system to discover contemporary art
The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists.
On the universality of the volatility formation process: when machine learning and rough volatility agree
We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks.
A characterisation of cross-impact kernels
Trading a financial asset pushes its price as well as the prices of other assets, a phenomenon known as cross-impact.
Deep calibration of the quadratic rough Heston model
The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm.
AHEAD : Ad-Hoc Electronic Auction Design
We introduce a new matching design for financial transactions in an electronic market.
On bid and ask side-specific tick sizes
The tick size, which is the smallest increment between two consecutive prices for a given asset, is a key parameter of market microstructure.
Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach
We consider the issue of a market maker acting at the same time in the lit and dark pools of an exchange.
From microscopic price dynamics to multidimensional rough volatility models
Rough volatility is a well-established statistical stylised fact of financial assets.
How to design a derivatives market?
We consider the problem of designing a derivatives exchange aiming at addressing clients needs in terms of listed options and providing suitable liquidity.
Optimal make take fees in a multi market maker environment
Following the recent literature on make take fees policies, we consider an exchange wishing to set a suitable contract with several market makers in order to improve trading quality on its platform.
Optimal auction duration: A price formation viewpoint
We compute the optimal duration of the auctions for 77 stocks traded on Euronext and compare the quality of price formation process under this optimal value to the case of a continuous limit order book.
Optimal make-take fees for market making regulation
We consider an exchange who wishes to set suitable make-take fees to attract liquidity on its platform.
An $\{l_1,l_2,l_{\infty}\}$-Regularization Approach to High-Dimensional Errors-in-variables Models
Under the first assumption, the rates of convergence of the proposed estimators depend explicitly on $\bar \delta$, while the second assumption has been applied when an estimator for the second moment of the observational error is available.
Sparse recovery under matrix uncertainty
We consider the model {eqnarray*}y=X\theta^*+\xi, Z=X+\Xi,{eqnarray*} where the random vector $y\in\mathbb{R}^n$ and the random $n\times p$ matrix $Z$ are observed, the $n\times p$ matrix $X$ is unknown, $\Xi$ is an $n\times p$ random noise matrix, $\xi\in\mathbb{R}^n$ is a noise independent of $\Xi$, and $\theta^*$ is a vector of unknown parameters to be estimated.
Statistics Theory Statistics Theory
