47 citations · 68 across the 10 of their papers we have counts for
13 papers
Target PCA: Transfer Learning Large Dimensional Panel Data
Junting Duan, Markus Pelger, Ruoxuan Xiong
This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data…
A Simple Method for Predicting Covariance Matrices of Financial Returns
Kasper Johansson, Mehmet Giray Ogut, Markus Pelger +2
We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling wi…
Inference for Large Panel Data with Many Covariates
Markus Pelger, Jiacheng Zou
This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detectio…
Bayesian Imputation with Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent problem in many prescriptive analytics models in operations management, healthcare and finance. Imputation methods for time-series data are…
Deep Learning Statistical Arbitrage
Jorge Guijarro-Ordonez, Markus Pelger, Greg Zanotti
Statistical arbitrage exploits temporal price differences between similar assets. We develop a unifying conceptual framework for statistical arbitrage and a novel data driven solut…
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent practical problem. Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for…
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Jason Yue Zhu, Yanling Cui, Yuming Liu +7
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks. Low latency variants of these models have also b…
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference
Ruoxuan Xiong, Markus Pelger
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose est…
Deep Learning in Asset Pricing
Luyang Chen, Markus Pelger, Jason Zhu
We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a ful…
Change-Point Testing for Risk Measures in Time Series
Lin Fan, Junting Duan, Peter W. Glynn +1
We propose novel methods for change-point testing for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series. We can detect genera…
State-Varying Factor Models of Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state…
Interpretable Sparse Proximate Factors for Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by…
On the existence of sure profits via flash strategies
Claudio Fontana, Markus Pelger, Eckhard Platen
We introduce and study the notion of sure profit via flash strategy, consisting of a high-frequency limit of buy-and-hold trading strategies. In a fully general setting, without im…