Modeling trading costs for correlated instruments to improve execution strategies.
problem Incorrect estimation of liquidity and suboptimal execution strategies due to neglecting cross-impact effects.
method Extending the linear propagator model to the multivariate case for correlated instruments, calibrating a cost model free of arbitrage and manipulation.
result Synchronizing the execution of correlated contracts is crucial for accurate liquidity estimation and optimal execution strategies.
Estimates linear model from noisy covariates and instruments using spectral regularization.
problem Estimating a linear model from many noisy covariates and instruments.
method Two-stage least squares with spectral regularization of canonical correlations.
result Upper and lower bounds on estimation error, proving optimality of the method with noisy data.
Researchers develop methods for causal inference with imperfect instrumental variables.
problem Quantifying cause and effect relationships with imperfect instrumental variables.
method Established a quantitative relationship between violations of instrumental inequalities and minimal measurement dependence, providing adapted inequalities valid in the presence of relaxed measurement dependence.
result Adapted inequalities for average causal effect in instrumental scenarios with binary outcomes, addressing violations of instrumental inequalities.
Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated wi…
Study on oil price's multifractal cross-correlations with other financial markets.
problem Analyzing statistical and multiscaling characteristics of oil prices and their cross-correlations with other financial instruments.
method Multifractal analysis, detrended cross-correlation coefficient, multifractal cross-correlation analysis.
result Multifractal cross-correlations between oil prices and other financial markets, especially with oil-producing countries' currencies.
We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the development of financial instruments, with a dynamical picture of an interacting market, in a simple setting. The proliferation of financial instruments apparently provides more means for risk diversification, making the market more efficient and…
Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…
In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other …
DeepPocket uses graph convolutional reinforcement learning for better financial portfolio management.
problem Maximizing return on investment while managing risk in correlated financial assets.
method Graph convolutional reinforcement learning framework with feature extraction, local information collection, and actor-critic reinforcement learning.
result DeepPocket outperformed market indexes on five real-life datasets over three investment periods, including during the Covid-19 crisis.
We analyze a method to produce pairs of non independent Poisson processes M(t),N(t) from positively correlated, self-decomposable, exponential renewals. In particular the present paper provides the family of copulas pairing the renewals, along with the closed form for the joint distribution pm,n(s,t) of the pair…
We propose two structural models for stochastic losses given default which allow to model the credit losses of a portfolio of defaultable financial instruments. The credit losses are integrated into a structural model of default events accounting for correlations between the default events and the associated losses. We…
DML-IV improves IV regression for learning decision policies by reducing bias.
problem Spurious correlations in offline datasets caused by hidden confounders.
method Double/debiased machine learning (DML) framework to reduce bias in two-stage IV regression.
result DML-IV outperforms state-of-the-art methods and learns high-performing policies.
Proposes a new estimator for weak instrumental variables in panel data models.
problem Weak instrumental variables due to ignored nonlinearities in panel data.
method Triangular simultaneous equation model with a nonlinear reduced form equation and a control function approach using Super Learner.
result The proposed SLCF estimator is consistent and asymptotically normal, achieving a parametric rate of convergence.
New method separates noisy auto-correlated components from multi-channel measurements.
problem Separating independent auto-correlated components from noisy multi-channel data.
method Simultaneous reconstruction and separation of components considering all channels, using information field theory.
result Significant improvement in signal-to-noise ratio, allowing separations even in high noise conditions.
Bitcoin's integration with major financial indices intensifies, suggesting a shift from alternative to integrated asset.
problem Understanding Bitcoin's evolving role in financial markets and its correlation dynamics.
method Rolling-window correlation, static correlation coefficients, and event-study framework on daily data from 2018 to 2025.
result Correlation levels between Bitcoin and major indices reached 0.87 in 2024, indicating a more integrated role.
Develops a hedging method for multi-asset derivatives with correlation risk.
problem Hedging multi-asset derivatives exposed to correlation and covariance risk.
method Combines dynamic trading with static hedging instruments using Galtchouk--Kunita--Watanabe decomposition.
result Explicit semi-static replication formulas for covariance swaps and geometric dispersion trades.
CgNN uses network structure as IVs to estimate causal effects in networks.
problem Hidden confounders complicate causal effect estimation in network data.
method CgNN combines GNNs and attention mechanisms to leverage network structure as IVs.
result CgNN effectively mitigates hidden confounder bias and improves causal effect estimation.
Study introduces a new copula-based measure for financial asset cointegration.
problem Traditional correlation coefficient's limitations in measuring financial asset relationships.
method Utilizes copulas to measure dependence among financial asset returns.
result Enhanced stability and informativeness in measuring financial asset relationships.
Paper extends Mean Field Game for portfolio trading, revealing trading flows impact on correlations.
problem Understanding how trading flows affect perceived correlations in portfolio trading.
method Extends Mean Field Game model to portfolios, analyzes real stock data, proposes calibration model.
result Closed-form formula linking observed correlations to underlying and initial imbalance of orders.
Proposes hedging strategies for mortgage prepayment risk.
problem Mortgage prepayment risk and its impact on financial institutions.
method Develops a model incorporating non-linear financial instruments for hedging.
result Shows non-linear risk in prepayment options and validates a functional form of prepayments.
Study analyzes correlation structure in two-factor Hull-White model for XVA calculations.
problem Capturing the correlation structure in two-factor Hull-White model for accurate XVA calculations.
method Combination of approximation formula and Monte-Carlo simulation to investigate correlation structure.
result Hull-White model effectively captures de-correlation of the yield curve under specific parameter conditions.
Cryptocurrencies are increasingly correlated with traditional financial markets.
problem Determining the independence of cryptocurrencies from traditional financial markets.
method High-frequency detrended cross-correlation analysis over various time scales and market periods.
result Cryptocurrencies have become more aligned with traditional financial markets, especially during bear phases.
Estimates long-term effects using past experiments as instruments with many weak instruments.
problem Estimating long-term causal effects with limited short-term outcomes and many weak instruments.
method Nonparametric instrumental variable inference with many weak instruments, using past experiments as instruments.
result Automatic debiased machine learning estimators for linear functionals of the structural function and its minimum-norm projection are efficient in the many-weak-instruments regime.
Ivy combines weak IV candidates to estimate causal effects robustly.
problem Estimating causal effects from observational data using weak or invalid IV candidates.
method Ivy synthesizes multiple weak IV candidates into a robust summary.
result Ivy produces more reliable causal effect estimates compared to allele scores.
ZNet learns instrumental representations from covariates for causal inference.
problem Lack of valid instruments in observational studies.
method Representation learning approach that constructs instrumental representations from observed covariates.
result ZNet enables IV-based estimation without explicit instruments.
Debias concept-based explanations by removing confounding information.
problem Correlation between concepts and confounding features.
method Causal prior graph and two-stage regression technique.
result Success in removing biases and improving concept ranking.
TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.
problem Estimating treatment effects with invalid instruments.
method Two-stage algorithm: first stage uses machine learning for nonlinearities, second stage selects and projects out instrument violations.
result Effective treatment effect estimation even with invalid instruments.
Paper proposes CIV estimator for categorical instruments in small sample settings.
problem Estimation with categorical instruments in settings with few observations per category.
method CIV estimator leveraging regularization assumption for latent categorical variable.
result CIV estimator is asymptotically normal, efficient, and semiparametrically efficient under homoskedasticity.
The Epps effect varies under different sampling schemes, affecting correlation emergence rates.
problem Uncertainty in choosing time and sampling rates for financial systems.
method Comparison of Epps effect under calendar, volume, and trade time schemes using a Hawkes process model.
result Correlations emerge faster under trade time compared to calendar time, and linearly under volume time.
Detects causal scenarios with inequality constraints among classical correlations.
problem Classifying causal structures and identifying those with inequality constraints.
method Using d-separation, e-separation, incompatible supports, and HLP condition.
result Resolved all but three causal scenarios with up to 4 observed variables.
The vast majority of market impact studies assess each product individually, and the interactions between the different order flows are disregarded. This strong approximation may lead to an underestimation of trading costs and possible contagion effects. Transactions in fact mediate a significant part of the correlatio…
Algorithm learns stock correlation matrix embedding using graph machine learning.
problem Understanding complex relationships among stocks based on their correlation matrix.
method Proposes a graph machine learning approach called Node2Vec to compress the correlation network into an embedding.
result The algorithm can learn an embedding from the correlation network of S&P 500 stock data.
New method uses few instruments to estimate complex causal effects.
problem Estimating causal effects with limited instruments in high-dimensional settings.
method Sequentially selects and combines instruments to estimate the treatment effect.
result Can reliably recover the treatment effect's projection onto the instrumented subspace.
Machine learning detectors can be accurate but introduce bias.
problem The accuracy of machine learning detectors affects the reliability of measured constructs.
method Examined the impact of detector accuracy on estimated correlations between constructs and phenomena.
result The expected correlation between phenomena decreases as detector accuracy decreases.
In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, w…
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…
Model disentangles timbre and pitch for musical instruments.
problem Learning disentangled representations of musical instrument sounds.
method Gaussian mixture variational autoencoders with two separate encoders for timbre and pitch.
result Model successfully disentangles timbre and pitch, enabling controllable synthesis and transfer.
Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.
problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.
Research predicts money market volume based on capital market and bank rates ratio.
problem Understanding the influence of capital market and bank rates on money market instruments.
method Correlation matrix and time series model to predict money market volume.
result Predictive model for money market instrument volume based on historical data.
DFIV uses deep neural nets to learn nonlinear features in IV regression.
problem Learning causal relationships from observational data with nonlinear interactions.
method DFIV trains deep neural nets to define nonlinear features on instruments and treatments, alternating training to compose stages 1 and 2.
result DFIV outperforms state-of-the-art methods on IV benchmarks and off-policy policy evaluation.
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment X and outcome Y cannot be blocked by other measurements, one possibility is to use an instrumental variable. In principle, it is possible under some…
SVM classifier outperforms other models in classifying musical instruments from IRMAS data.
problem Musical instrument classification from audio signals.
method Implemented supervised (SVM) and unsupervised (Hierarchical Clustering) learning algorithms.
result SVM classifier achieved 79% accuracy on IRMAS data.
Valid causal inference with invalid instruments using majority or modal valid relationships.
problem Estimating causal effects in the presence of unobserved confounding and invalid instruments.
method Ensemble of instrumental variable estimators to estimate the modal prediction, achieving accurate estimates of conditional average treatment effects.
result Valid causal inference can be achieved with a majority or modal valid instrument-response relationship.
Scattering transform improves note onset detection and instrument recognition in music transcription.
problem Note onset detection and instrument recognition in music transcription.
method Multiscale scattering operators applied to MIDI-driven datasets and real musical pieces.
result Scattering transform outperforms other sound representations for note onset detection and instrument recognition.
Bayesian nonparametric machine learning improves instrumental variable inference.
problem Estimating causal effects with nonlinear relationships.
method Bayesian Additive Regression Trees (BART) for estimating functions and Dirichlet Process mixtures for error terms.
result Dramatic improvements in inference with nonlinear data, no manual tuning required.
Proposes new financial instruments for better market hedging.
problem Market crashes detection and hedging.
method Introduces perpetual derivatives for hedging jump-diffusion and stochastic volatility models.
result New financial instruments can serve as an early warning system for market crashes.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
problem Endogeneity bias and invalid instrument validation in high-dimensional data.
method Merge variable selection algorithms and probabilistic graphs to estimate house prices and causal structure.
result Efficient data-driven instrument selection and invalid instrument purge in high-dimensional data.
Proposes TSCI method to infer causal effects with weak or invalid instruments using machine learning.
problem Causal inference with weak or invalid instrumental variables.
method Two-stage curvature identification (TSCI) using machine learning.
result Asymptotically unbiased and Gaussian estimator for causal effects.