Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Pelger, Markus"'
In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, a
Externí odkaz:
http://arxiv.org/abs/2403.14067
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 sets. We refer to our estimator as target-PCA. Transfer learning from
Externí odkaz:
http://arxiv.org/abs/2308.15627
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 window or exponentially weighted moving average (EWMA) to more sophistic
Externí odkaz:
http://arxiv.org/abs/2305.19484
Autor:
Pelger, Markus, Zou, Jiacheng
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 detection control while having higher power than existing approaches. We devel
Externí odkaz:
http://arxiv.org/abs/2301.00292
Publikováno v:
In Journal of Econometrics September 2024 244(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 usually applied to the full panel data with the purpose of training a
Externí odkaz:
http://arxiv.org/abs/2202.00871
Statistical arbitrage exploits temporal price differences between similar assets. We develop a unifying conceptual framework for statistical arbitrage and a novel data driven solution. First, we construct arbitrage portfolios of similar assets as res
Externí odkaz:
http://arxiv.org/abs/2106.04028
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 a downstream out-of-sample task. For example, in finance, imputation o
Externí odkaz:
http://arxiv.org/abs/2102.12736
Autor:
Zhu, Jason Yue, Cui, Yanling, Liu, Yuming, Sun, Hao, Li, Xue, Pelger, Markus, Yang, Tianqi, Zhang, Liangjie, Zhang, Ruofei, Zhao, Huasha
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 been developed in recent years in order to apply them in the field of s
Externí odkaz:
http://arxiv.org/abs/2101.06323
Publikováno v:
In Journal of Financial Economics October 2023 150(1):94-138