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CDS Proxy Construction via Machine Learning Techniques Part II : Parametrization, Correlation, Benchmarking
Brummelhuis, Raymond - 2019
This article is the second of two articles by the authors on the construction of CDS proxy rates. In the first article, the authors proposed a machine learning (ML) -based proxy-rate construction technique which uses classification to construct so-called Proxy-Names whose liquidly quoted CDS...
Persistent link: https://www.econbiz.de/10012892865
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Improving Portfolio Allocation Through Covariance Matrix Filtering
Golden, Daron - 2017
eigen-structure of the sample correlation matrix. This paper compares the filtering methods using a theoretical eigen … are identified. The sample correlation matrix suffers from excess dispersion in its eigenvalues and excess dispersion in … the sample covariance matrix, its diagonal elements and the covariance matrix based on the constant correlation model …
Persistent link: https://www.econbiz.de/10012965654
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Filtering Noise from Correlation Matrices (Detection of Correlation Diversion, Pairs Trading, Risk Analysis, Et Al.)
Izmailov, Alexander - 2014
Demonstration of the omnipresence of noise in financial correlation/covariance matrices revealed by means of random … matrix theory, a branch of probability theory.Introduction of the Shannon entropy as a measure of noise in correlation … filtering noise from correlation matrices. This noise is the inevitable consequence of the imperfection of traditional modeling …
Persistent link: https://www.econbiz.de/10013060895
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Dramatically Improved Portfolio Optimization Results with Noise-Filtered Covariance : Paper 6 in a Series 'Uncovering Hidden Dependencies'
Izmailov, Alexander - 2014
filtering noise from correlation matrices developed by the New York based company, Market Memory Trading, L.L.C. (MMT …
Persistent link: https://www.econbiz.de/10013062135
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How to combine a billion alphas
Kakushadze, Zura; Yu, Willie - In: The journal of asset management 18 (2017) 1, pp. 64-80
Persistent link: https://www.econbiz.de/10011592763
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Machine learning risk models
Kakushadze, Zura; Yu, Willie - In: Journal of risk & control 6 (2019) 1, pp. 37-64
Persistent link: https://www.econbiz.de/10012237599
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Diversification benefits of commodities : a stochastic dominance efficiency approach
Daskalaki, Charoula; Skiadopoulos, George; Topaloglou, … - 2016
We revisit the question whether commodities should be included in investors' portfolios. We employ for the first time a stochastic dominance efficiency (SDE) approach to construct optimal portfolios with and without commodities and we evaluate their comparative performance. SDE circumvents the...
Persistent link: https://www.econbiz.de/10011473894
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Machine Forecast Disagreement
Bali, Turan G.; Kelly, Bryan T.; Mörke, Mathis; … - National Bureau of Economic Research - 2023
We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...
Persistent link: https://www.econbiz.de/10014337816
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Should Investors Choose Both the Highest-Return and Small-Variance Assets When the Mean-Variance Rule Says No? A Study of Health Care and T-Bill in the Investment
Lv, Zhihui - 2020
market. This sector however has the highest mean in our study and a low correlation with the business cycle. On the other …
Persistent link: https://www.econbiz.de/10012841796
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Diversification Benefits of Commodities : A Stochastic Dominance Efficiency Approach
Daskalaki, Charoula - 2018
We revisit the question whether commodities should be included in investors' portfolios. We employ for the first time a stochastic dominance efficiency (SDE) approach to construct optimal portfolios with and without commodities and we evaluate their comparative performance. SDE circumvents the...
Persistent link: https://www.econbiz.de/10012930468