Jackknife Estimator for Tracking Error Variance of Optimal Portfolios

2009 ◽  
Vol 55 (6) ◽  
pp. 990-1002 ◽  
Author(s):  
Gopal K. Basak ◽  
Ravi Jagannathan ◽  
Tongshu Ma
Keyword(s):  
Tracking Error ◽  
Error Variance ◽  
Optimal Portfolios ◽  
Jackknife Estimator

scholarly journals Estratégia Long-Short, Neutra ao Mercado, e Index Tracking Baseadas em Portfólios Cointegrados

2010 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
João Frois Caldeira ◽  
Marcelo Savino Portugal
Keyword(s):  
Ad Hoc ◽  
Tracking Error ◽  
Covariance Matrices ◽  
Return Level ◽  
Index Tracking ◽  
Optimal Portfolios ◽  
The Mean ◽  
Efficient Allocations ◽  
Mean Variance ◽  
Tracking Portfolios

The traditional models to optimize portfolios based on mean-variance analysis aim to determine the portfolio weights that minimize the variance for a certain return level. The covariance matrices used to optimize are difficult to estimate and ad hoc methods often need to be applied to limit or smooth the mean-variance efficient allocations recommended by the model. Although the method is efficient, the tracking error isn’t certainly stationary, so the portfolio can get distant from the benchmark, requiring frequent re-balancements. This work uses cointegration methodology to devise two quantitative strategies: index tracking and long-short market neutral. We aim to design optimal portfolios acquiring the asset prices’ co-movements. The results show that the devise of index tracking portfolios using cointegration generates goods results, replicating the benchmark’s return and volatility. The long-short strategy generated stable returns under several market circumstances, presenting low volatility.

scholarly journals Serial correlation and TEV bias in index funds

2003 ◽  
Vol 34 (2) ◽  
pp. 45-53 ◽  
Author(s):  
H. Raubenheimer
Keyword(s):  
High Frequency ◽  
Serial Correlation ◽  
Tracking Error ◽  
Error Variance ◽  
Value Proposition ◽  
Fund Manager ◽  
Index Funds ◽  
Fund Managers ◽  
The Difference

Index or passive fund managers and investors analyse the interim volatility of the difference between their fund’s returns and the index’s returns, i.e. the fund’s tracking error variance** (TEV) in order to monitor the success with which tracker funds mimic their benchmark. The objective of a passive or index fund manager should be to keep TEV as close to zero as possible. Pope and Yadav (1994) show that an index fund that is overweight relative to it’s index in either relatively less or relatively more liquid stocks, is expected to exhibit negative serial correlation in its TE’s. Consequently, estimates of TEV will be upwardly biased, particularly when using high frequency (such as daily or weekly) data.This article finds evidence of negative serial correlation in the weekly, monthly and quarterly TE’s of domestic index funds. Consequently it is shown that TEV will likely be overestimated. There are two important implications of this upward bias in TEV estimation. Firstly, index funds, which are expected to offer close to zero benchmark-relative or active risk, may appear far more ‘risky’ than they actually are thus damaging their value-proposition to investors. Secondly, when funds appear to have greater TEV than they actually do, the manager may ‘churn’ the fund’s assets more than necessary in order to bring the fund back into alignment with its index thus incurring greater and unnecessary transaction costs.The analyses in this article therefore suggest that TE measurements should be examined for negative serial correlation before estimates of TEV are made. If serial correlation is detected, estimates of TEV should either be made from lower frequency, uncorrelated TE measurements, if they are available, or an adjustment technique such as the Lo-MacKinlay adjustment should be applied to correct for the bias in TEV estimation.

A Time-Varying Generalized Minimum Variance Controller for Servo Application

2013 ◽  
Vol 278-280 ◽  
pp. 1403-1408 ◽  
Author(s):  
Zheng Li
Keyword(s):  
Performance Index ◽  
Linear Time ◽  
Tracking Error ◽  
Error Variance ◽  
Minimum Variance ◽  
Time Varying ◽  
Output Tracking ◽  
Time Varying Systems ◽  
Control Objective ◽  
Process Disturbances

A generalized minimum variance controller is developed for linear time-varying systems for servo applications. The plants to be controlled is described using a SISO CARMA model and the control objective is to minimize a generalized minimum variance performance index, where the output tracking error variance is penalized by squared incremental of plant input in order to reduce fluctuation in plant input and attenuate process disturbances.

Assessing estimation error in a tracking error variance minimisation framework

1998 ◽  
Vol 6 (1-2) ◽  
pp. 175-192
Author(s):  
David M. Walsh ◽  
Kathleen D. Walsh ◽  
John P. Evans
Keyword(s):  
Estimation Error ◽  
Tracking Error ◽  
Error Variance

scholarly journals A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&P 100 INDEX

2013 ◽  
Vol 14 (4) ◽  
pp. 758-775 ◽  
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Ismael Moya
Keyword(s):  
Portfolio Management ◽  
Programming Model ◽  
Tracking Error ◽  
Error Variance ◽  
Index Tracking ◽  
Mathematical Programming Model ◽  
Substantial Cost ◽  
Risk Return ◽  
Wide Range ◽  
Reference Index

Index tracking seeks to minimize the unsystematic risk component by imitating the movements of a reference index. Partial index tracking only considers a subset of the stocks in the index, enabling a substantial cost reduction in comparison with full tracking. Nevertheless, when heterogeneous investment profiles are to be satisfied, traditional index tracking techniques may need different stocks to build the different portfolios. The aim of this paper is to propose a methodology that enables a fund's manager to satisfy different clients’ investment profiles but using in all cases the same subset of stocks, and considering not only one particular criterion but a compromise between several criteria. For this purpose we use a mathematical programming model that considers the tracking error variance, the excess return and the variance of the portfolio plus the curvature of the tracking frontier. The curvature is not defined for a particular portfolio, but for all the portfolios in the tracking frontier. This way funds’ managers can offer their clients a wide range of risk-return combinations just picking the appropriate portfolio in the frontier, all of these portfolios sharing the same shares but with different weights. An example of our proposal is applied on the S&P 100.

scholarly journals Optimal omega-ratio portfolio performance constrained by tracking error

2020 ◽  
Vol 17 (3) ◽  
pp. 263-280
Author(s):  
Wade Gunning ◽  
Gary van Vuuren
Keyword(s):  
Recent Work ◽  
Tracking Error ◽  
Sharpe Ratio ◽  
Main Axis ◽  
Portfolio Performance ◽  
Optimal Portfolios ◽  
Investment Style ◽  
The Mean ◽  
Omega Ratio ◽  
Mean Variance

The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.

scholarly journals Mutual Fund Trades: Timing Sentiment and Managing Tracking Error Variance

2012 ◽  
Author(s):  
Dominic Gasbarro ◽  
Grant Stewart Cullen ◽  
Gary S. Monroe ◽  
J. Kenton Zumwalt
Keyword(s):  
Mutual Fund ◽  
Tracking Error ◽  
Error Variance

scholarly journals Mitigating high latitude ionospheric scintillation effects on GNSS Precise Point Positioning exploiting 1-s scintillation indices

2021 ◽  
Vol 95 (3) ◽  
Author(s):  
Kai Guo ◽  
Sreeja Vadakke Veettil ◽  
Brian Jerald Weaver ◽  
Marcio Aquino
Keyword(s):  
High Latitude ◽  
Precise Point Positioning ◽  
Tracking Error ◽  
Error Variance ◽  
Satellite System ◽  
The Arctic ◽  
Ionospheric Scintillation ◽  
Positioning Errors ◽  
Point Positioning ◽  
Scintillation Indices

AbstractIonospheric scintillation refers to rapid and random fluctuations in radio frequency signal intensity and phase, which occurs more frequently and severely at high latitudes under strong solar and geomagnetic activity. As one of the most challenging error sources affecting Global Navigation Satellite System (GNSS), scintillation can significantly degrade the performance of GNSS receivers, thereby leading to increased positioning errors. This study analyzes Global Positioning System (GPS) scintillation data recorded by two ionospheric scintillation monitoring receivers operational, respectively, in the Arctic and northern Canada during a geomagnetic storm in 2019. A novel approach is proposed to calculate 1-s scintillation indices. The 1-s receiver tracking error variances are then estimated, which are further used to mitigate the high latitude scintillation effects on GPS Precise Point Positioning. Results show that the 1-s scintillation indices can describe the signal fluctuations under scintillation more accurately. With the mitigation approach, the 3D positioning error is greatly reduced under scintillation analyzed in this study. Additionally, the 1-s tracking error variance achieves a better performance in scintillation mitigation compared with the previous approach which exploits 1-min tracking error variance estimated by the commonly used 1-min scintillation indices. This work is relevant for a better understanding of the high latitude scintillation effects on GNSS and is also beneficial for developing scintillation mitigation tools for GNSS positioning.

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