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Analysing Global Fixed Income Markets with Tensors

Bruno Scalzo Dees

arXiv 6 Aug 2019 · Finance — Portfolio Management · 1 citations (OpenAlex)

arXiv:1908.02101 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce a tensor-valued approach to model the global risks shared by multiple interest rate curves. In this way, the estimated risk factors can be analytically decomposed into maturity-domain and country-domain constituents, which allows the investor to devise rigorous and tractable global portfolio management and hedging strategies tailored to each risk domain. An empirical analysis confirms the existence of global risk factors shared by eight developed economies, and demonstrates their ability to compactly describe the global macroeconomic environment.

Citation extraction

25
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29
in-text mentions
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distinct cited
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self-citations
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1I. Kisil, B. Scalzo Dees, A. Moniri, G. G. Calvi, and D. P. Mandic,… HOTTBOX: Higher Order Tensor ToolBOX0.64422100%
2T. G. Kolda and B. W. Bader, “Tensor Decompositions and Applications… (2009) Tensor Decompositions and Applications0.64422100%
3A. Cichocki, D. P. Mandic, A. H. Phan, C. F. Caiafa, G. Zhou, Q. Zha… (2015) Tensor Decompositions for Signal Processing Applications0.64422100%
4B. Scalzo Dees and D. P. Mandic, “A Statistically Identifiable Model… (2019) A Statistically Identifiable Model for Tensor-Valued Gaussian Random Variables0.64422100%
5“PCA Unleashed,” Research Report, Credit Suisse (2015) PCA Unleashed0.40511100%
6L. De Lathauwer, B. D. Moor, and J. Vandewalle, “A Multilinear Singu… (2000) A Multilinear Singular Value Decomposition0.40511100%
7J. Driessen, B. Melenberg, and T. Nijman, “Common Factors in Interna… (2003) Common Factors in International Bond Returns0.40511100%
8B. Flury, Common Principal Components and Related Multivariate Model… (1988)0.40511100%
9P. D. Hoff, “Separable Covariance Arrays via the Tucker Product, wit… (2011) Separable Covariance Arrays via the Tucker Product, with Applications to Multivariate Relational Data0.40511100%
10I. T. Jolliffe, Principal Component Analysis. 1em plus 0.5em minus 0… (1986)0.40511100%

Showing the top 10 of 25 scored citations.