Forecasting covariance for optimal carry trade portfolio allocations

Ames, Matthew M.; Bagnarosa, Guillaume; Peters, Gareth W.; Shevchenko, Pavel V.; Matsui, Tomoko

doi:10.1109/icassp.2017.7953290

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Before the recent developments of covariance regression methods such as those in Hoff & Niu (2012) and the applications in financial settings using these methods such as those in Ames et al (2017) and(2018), the most common approach to covariance forecasting was using either empirical historical sample estimators as predictions based on the past realized portfolio asset returns covariance estimates (over windows of varying length) used as the best estimate for the forthcoming covariance. Alternatively, for model-based forecasting methods, it is common to utilize methods from multivariate time series such as DCC-MGARCH.…”

Section: Actuarial Setting and Contextmentioning

confidence: 99%

Package CovRegpy: Regularized covariance regression and forecasting in Python

van Jaarsveldt,

Peters,

Ames

et al. 2024

Ann. actuar. sci.

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This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management. The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py, CovRegpy_DCC.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica, 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease. The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter’s covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy.

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Section: Actuarial Setting and Contextmentioning

confidence: 99%