scholarly journals Multistage allocation problem for Mexican pension funds

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249857
Author(s):  
Andrés García-Medina ◽  
Norberto A. Hernández-Leandro ◽  
Graciela González Farías ◽  
Nelson Muriel
Keyword(s):  
Covariance Matrix ◽  
Pension System ◽  
Expected Returns ◽  
Entropy Measure ◽  
Computationally Efficient ◽  
Risky Assets ◽  
Glide Path ◽  
Target Date ◽  
Bootstrap Model ◽  
Glide Paths

The problem of multistage allocation is solved using the Target Date Fund (TDF) strategy subject to a set of restrictions which model the latest regulatory framework of the Mexican pension system. The investment trajectory or glide-path for a representative set of 14 assets of heterogeneous characteristics is studied during a 161 quarters long horizon. The expected returns are estimated by the GARCH(1,1), EGARCH(1,1), GJR-GARCH(1,1) models, and a stationary block bootstrap model is used as a benchmark for comparison. A fixed historical covariance matrix and a multi-period estimation of DCC-GARCH(1,1) are also considered as inputs of the objective function. Forecasts are evaluated through their asymmetric dependencies as quantified by the transfer entropy measure. In general, we find very similar glide-paths so that the overall structure of the investment is maintained and does not rely on the particular forecasting model. However, the GARCH(1,1) under a fixed historical covariance matrix exhibits the highest Sharpe ratio and in this sense represents the best trade-off between wealth and risk. As expected, the initial stages of the obtained glide-paths are initially dominated by risky assets and gradually transition into bonds towards the end oof the trajectory. Overall, the methodology proposed here is computationally efficient and displays the desired properties of a TDF strategy in realistic settings.

DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH

2021 ◽  
pp. 1-26
Author(s):  
Jin Sun ◽  
Dan Zhu ◽  
Eckhard Platen
Keyword(s):  
Asset Allocation ◽  
Dynamic Asset Allocation ◽  
Growth Optimal Portfolio ◽  
Risky Assets ◽  
Target Date ◽  
Target Date Funds ◽  
Asset Allocations ◽  
Investment Choices ◽  
Glide Paths

ABSTRACT Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

scholarly journals Applications of an Infrared Doppler Lidar in Detection of Wind Shear

2008 ◽  
Vol 25 (5) ◽  
pp. 637-655 ◽  
Author(s):  
C. M. Shun ◽  
P. W. Chan
Keyword(s):  
Hong Kong ◽  
Wind Shear ◽  
Spatial And Temporal Variability ◽  
Doppler Lidar ◽  
Glide Path ◽  
Alerting System ◽  
Hong Kong International Airport ◽  
Detection Technologies ◽  
Gust Front ◽  
Glide Paths

Abstract In December 2005, operational wind shear alerting at the Hong Kong International Airport (HKIA) reached an important milestone with the launch of the automatic Lidar (light detection and ranging) Windshear Alerting System (LIWAS). This signifies that the anemometer-based and radar-based wind shear detection technologies deployed worldwide in the twentieth century have been further advanced by the addition of the lidar—a step closer to all-weather coverage. Unlike the microburst and gust front, which have a well-defined coherent vertical structure in the lowest several hundred meters of the atmosphere, terrain-induced wind shear tends to have high spatial and temporal variability. To detect the highly changeable winds to be encountered by the aircraft under terrain-induced wind shear situations, the Hong Kong Observatory devises an innovative glide path scan (GPScan) strategy for the lidar, pointing the laser beam toward the approach and departure glide paths, with the changes in azimuth and elevation angles concerted. The purpose of the GPScans is to derive the headwind profiles and hence the wind shear along the glide paths. Developed based on these GPScans, LIWAS is able to capture about 76% of the wind shear events reported by pilots over the most-used approach corridor under clear-air conditions. During the past two years, further developments of the lidar took place at HKIA, including the use of runway-specific lidar to further enhance the wind shear detection performance.

Beyond the Glide Path: The Drivers of Target-Date Fund Returns

2018 ◽  
Vol 5 (4) ◽  
pp. 25-39
Author(s):  
David Blanchett ◽  
Paul D. Kaplan
Keyword(s):  
Glide Path ◽  
Target Date

Glide Paths Based on a Retirement Goal and Depleting Human Capital

2019 ◽  
Keyword(s):  
Human Capital ◽  
Life Cycle ◽  
Asset Allocation ◽  
Retirement Planning ◽  
Retirement Savings ◽  
Financial Assets ◽  
Dynamic Asset Allocation ◽  
Glide Path ◽  
Input Variables ◽  
Glide Paths

The retirement goals of many Americans are underfunded. The problem is compounded by the complexity of self-managing distribution portfolios, particularly as DC plans replace DB plans. We believe most retirement glide paths are satisfactory but suboptimal solutions. We introduce a glide path of financial assets over the life cycle based on a retirement goal and depleting human capital. The method is anchored to the foundational principles of intertemporal portfolio theory while borrowing heavily from goals-based asset allocation. The result is a dynamic asset allocation over the life cycle that is a function of critical input variables relevant to retirement planning such as retirement savings, retirement consumption and risk aversion. The glide path can be customized to individuals, or semi-customized to discrete subpopulations of DC plan participants.

Evaluation of Target-Date Glide Paths withinDefined Contribution Plans

2014 ◽  
Vol 1 (4) ◽  
pp. 75-94 ◽  
Author(s):  
Richard K. Fullmer ◽  
James A. Tzitzouris
Keyword(s):  
Target Date ◽  
Glide Paths

scholarly journals Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 136
Author(s):  
Pan Gong ◽  
Xixin Chen
Keyword(s):  
Covariance Matrix ◽  
Lower Boundary ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

scholarly journals Q-Factors and Investment CAPM

2020 ◽  
Author(s):  
Lu Zhang
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Explanatory Power ◽  
Disruptive Innovation ◽  
Expected Returns ◽  
Size Factor ◽  
Efficient Markets ◽  
Present Value ◽  
Risky Assets ◽  
High Investment

The Hou–Xue–Zhang q-factor model says that the expected return of an asset in excess of the risk-free rate is described by its sensitivities to the market factor, a size factor, an investment factor, and a return on equity (ROE) factor. Empirically, the q-factor model shows strong explanatory power and largely summarizes the cross-section of average stock returns. Most important, it fully subsumes the Fama–French 6-factor model in head-to-head spanning tests. The q-factor model is an empirical implementation of the investment-based capital asset pricing model (the Investment CAPM). The basic philosophy is to price risky assets from the perspective of their suppliers (firms), as opposed to their buyers (investors). Mathematically, the investment CAPM is a restatement of the net present value (NPV) rule in corporate finance. Intuitively, high investment relative to low expected profitability must imply low costs of capital, and low investment relative to high expected profitability must imply high costs of capital. In a multiperiod framework, if investment is high next period, the present value of cash flows from next period onward must be high. Consisting mostly of this next period present value, the benefits to investment this period must also be high. As such, high investment next period relative to current investment (high expected investment growth) must imply high costs of capital (to keep current investment low). As a disruptive innovation, the investment CAPM has broad-ranging implications for academic finance and asset management practice. First, the consumption CAPM, of which the classic Sharpe–Lintner CAPM is a special case, is conceptually incomplete. The crux is that it blindly focuses on the demand of risky assets, while abstracting from the supply altogether. Alas, anomalies are primarily relations between firm characteristics and expected returns. By focusing on the supply, the investment CAPM is the missing piece of equilibrium asset pricing. Second, the investment CAPM retains efficient markets, with cross-sectionally varying expected returns, depending on firms’ investment, profitability, and expected growth. As such, capital markets follow standard economic principles, in sharp contrast to the teachings of behavioral finance. Finally, the investment CAPM validates Graham and Dodd’s security analysis on equilibrium grounds, within efficient markets.

Why Pairing LDI with De-Risking Glide Paths Produces Inferior Pension Fund Outcomes

2018 ◽  
Keyword(s):  
Pension Fund ◽  
Pension Plan ◽  
Pension Funds ◽  
Conventional Wisdom ◽  
Alternative Solution ◽  
Credit Risks ◽  
Glide Path ◽  
Funded Status ◽  
Illiquid Assets ◽  
Glide Paths

Combining traditional Liability Driven Investment (LDI) with funded status responsive de-risking strategies involves inconsistent treatment of risks in these two elements of what has become a popular pension strategy. This inconsistency causes irreconcilable conflicts in their execution and imperils the positive pension fund outcome. This article provides a critique of the combined LDI/De-risking Glide Path strategy as currently implemented by many pension plan managers and also provides an example of an alternative solution that improves pension plan outcomes. Our prescription for the pension de-risking glide path approach differs from conventional wisdom, resulting in faster de-risking, without undesirable market betas that are unrelated to the liability. It also avoids illiquid assets that pension funds often gravitate toward in their quest for returns, takes fewer credit risks, and seeks more alpha risks.

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