Timberland Indexes and Portfolio Management

1990 ◽  
Vol 14 (3) ◽  
pp. 119-124 ◽  
Author(s):  
F. Christian Zinkhan ◽  
Kossuth Mitchell
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Financial Assets ◽  
Southern Pine ◽  
Investment Performance ◽  
Risk Return ◽  
Efficient Portfolios ◽  
The Mean ◽  
Mean Risk

Abstract This paper explores two timberland index applications: asset allocation and investment performance evaluation. The Southern Timberland Index Fund (STIF), a southern pine index fund, is adopted for use in these applications. In the asset allocation application, the mean risk of risk-return efficient portfolios containing financial assets and the STIF is discovered to be 43% less than the mean risk of the efficient portfolios containing only financial assets. Efficient portfolios contain the STIF in proportions as high as almost 30%. As far as performance is concerned, a timberland index is suggested for use as a benchmark for evaluating (1) timberland investment managers and (2) the investment performance of timberland versus other investment alternatives. Before such applications become commonplace, it is concluded that problems associated with existing timberland indexes be addressed. South. J. Appl. For. 14(3):119-124.

Portfolio selection using the Riskiness Index

2018 ◽  
Vol 35 (2) ◽  
pp. 330-339 ◽  
Author(s):  
Doron Nisani
Keyword(s):  
Portfolio Selection ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Financial Risk ◽  
Order Approximation ◽  
Approximation Order ◽  
Content Type ◽  
Normal Inverse Gaussian Distribution ◽  
Efficient Portfolios ◽  
The Mean

PurposeThe purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.Design/methodology/approachThis paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.FindingsThis paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).Research limitations/implicationsThe Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.Originality/valueThe mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.

Herding and Delegated Portfolio Management: The Impact of Relative Performance Evaluation on Asset Allocation

2011 ◽  
Vol 01 (02) ◽  
pp. 265-292 ◽  
Author(s):  
Ernst Maug ◽  
Narayan Naik
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Relative Performance ◽  
Optimal Contracts ◽  
Fund Managers ◽  
Portfolio Managers ◽  
Delegated Portfolio Management ◽  
The Impact ◽  
Allocation Decisions

This paper investigates the effect of fund managers' performance evaluation on their asset allocation decisions. We derive optimal contracts for delegated portfolio management and show that they always contain relative performance elements. We then show that this biases fund managers to deviate from return-maximizing portfolio allocations and follow those of their benchmark (herding). In many cases, the trustees of the fund who employ the fund manager prefer such a policy. We also show that fund managers in some situations ignore their own superior information and "go with the flow" in order to reduce deviations from their benchmark. We conclude that incentive provisions for portfolio managers are an important factor in their asset allocation decisions.

scholarly journals Portfolio Selection Models

2017 ◽  
pp. 43-82
Keyword(s):  
Decision Theory ◽  
Portfolio Selection ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Computer Programs ◽  
Var Analysis ◽  
Widespread Acceptance ◽  
Portfolio Manager ◽  
The Mean ◽  
Mean Variance

The main goal behind the concept of portfolio management is to combine various assets into portfolios and then to manage those portfolios so as to achieve the desired investment objectives. To be more specific, the investors' needs are mostly defined in terms of profit and risk, and the portfolio manager makes a sound decision aimed ether to maximize the return or minimize the risk. The Mean-Variance and Mean-VaR analysis has gained widespread acceptance among practitioners of asset allocation. Although they are the simplest models of investment, sometimes they are sufficiently rich to be directly useful in applied problems and decision theory. Here you will learn how to apply these analyses in practice using computer programs and spreadsheets.

scholarly journals A Fund-Based Timberland Investment Performance Measure and Implications for Asset Allocation

1998 ◽  
Vol 22 (3) ◽  
pp. 143-147 ◽  
Author(s):  
Jon P. Caulfield
Keyword(s):  
Asset Allocation ◽  
Institutional Investors ◽  
Performance Index ◽  
Historical Record ◽  
Performance Measure ◽  
Investment Management ◽  
Investment Performance ◽  
Risk Return ◽  
Asset Value ◽  
Investment Management Companies

Abstract Timberland investment management companies and institutional investors use indexes to calculate the performance of timberland investments. Most indexes are based on hypothetical timberland properties. The Timberland Performance Index (TPI), a fund-based performance measure, provides composite returns for actual, institutionally owned timberlands. The TPI has several desirable attributes: it uses publicly available data from real properties, is weighted by asset value, has a sufficiently long historical record that meaningful comparisons can be made with other assets, and can be updated quarterly. The TPI is employed to demonstrate how adding timberland to a portfolio influences risk-return relationships for institutional portfolios. For the 1981-1996 period it is found that adding timberland tends to enhance returns for given levels of risk. This is consistent with previous research, which employed hypothetical timberland indexes for this purpose. South. J. Appl. For. 22(3):143-147.

scholarly journals Strategic Asset Allocation Of Credit Guarantors

2015 ◽  
Vol 31 (5) ◽  
pp. 1823
Author(s):  
Dong-Woo Rhee ◽  
Hyoung-Goo Kang ◽  
Soo-Hyun Kim
Keyword(s):  
Public Policy ◽  
Asset Allocation ◽  
Minimum Variance ◽  
Risk Return ◽  
Out Of Sample ◽  
Sample Test ◽  
Prior Literature ◽  
The Mean ◽  
Risk Contribution ◽  
Mean Variance

<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

Analysis on the Risk Return Profile of Alternative Assets Under Reference Portfolio Concept

2019 ◽  
Vol 27 (2) ◽  
pp. 193-209
Author(s):  
Su Jin Lee ◽  
Jin Wan Cho ◽  
Jae Hyun Lee
Keyword(s):  
Asset Allocation ◽  
Opportunity Costs ◽  
Strategic Asset Allocation ◽  
Alternative Investments ◽  
Risk Return ◽  
Benchmark Portfolio ◽  
The Mean ◽  
Asset Classes ◽  
Relationship Of ◽  
Mean Variance

This paper provides the methodology of estimating the risk-return relationship of alternative asset investments within the mean-variance framework. While conducting strategic asset allocation, most of the institutional investors do not take into account the risk-return relationship of alternative assets, or use arbitrary policy numbers that do not properly reflect the characteristics of alternative assets. This paper borrows the concept of reference portfolio in developing the methodology of estimating the risk-return relationship of alternative investments. The reference portfolio is the benchmark portfolio used in strategic asset allocation by pension funds. This can serve as the opportunity costs of alternative investments. We use the realized IRR’s from actual investments, and estimate the risk-return characteristics of alternative investments. We find that by properly estimating the mapping relationship between the reference portfolio and alternative asset classes, we can incorporate the risk-return profile of these non-market assets within the mean-variance framework together with the other traditional asset classes.

A LARGE DEVIATION APPROACH TO PORTFOLIO MANAGEMENT

2000 ◽  
Vol 03 (04) ◽  
pp. 617-639 ◽  
Author(s):  
L. GARDIOL ◽  
R. GIBSON ◽  
P.-A. BARES ◽  
R. CONT ◽  
S. GYGER
Keyword(s):  
Portfolio Management ◽  
Large Deviation ◽  
Asset Returns ◽  
Portfolio Allocation ◽  
Investment Horizon ◽  
Financial Assets ◽  
Large Deviations Theory ◽  
The Mean ◽  
Mean Variance ◽  
New Framework

We propose a new framework to measure the risk of a single asset and of a portfolio of financial assets which takes the agent's investment horizon into account. The methodology is based on the moderate and large deviations theory in its simplest form. We show how it can be used to select optimal portfolios given investors' planning horizons and preferences for fatter right or left tails. For practical purposes, we introduce a new parameter, the "dilation exponent" α to characterize asset returns' distributions beyond the information contained in the mean-variance framework. We estimate α for Swiss individual stocks and for MSCI country and sector stock market indices. Finally, we show how to use the dilation exponent in conjunction with Sharpe's ratio for portfolio allocation purposes.

scholarly journals Usage of Cholesky Decomposition in order to Decrease the Nonlinear Complexities of Some Nonlinear and Diversification Models and Present a Model in Framework of Mean-Semivariance for Portfolio Performance Evaluation

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
H. Siaby-Serajehlo ◽  
M. Rostamy-Malkhalifeh ◽  
F. Hosseinzadeh Lotfi ◽  
M. H. Behzadi
Keyword(s):  
Performance Evaluation ◽  
Stock Returns ◽  
Covariance Matrix ◽  
Nonlinear Models ◽  
Cholesky Decomposition ◽  
Portfolio Performance ◽  
Financial Assets ◽  
The Mean ◽  
And Performance ◽  
Higher Sensitivity

In order to get efficiency frontier and performance evaluation of portfolio, nonlinear models and DEA nonlinear (diversification) models are mostly used. One of the most fundamental problems of usage of nonlinear and diversification models is their computational complexity. Therefore, in this paper, a method is presented in order to decrease nonlinear complexities and simplify calculations of nonlinear and diversification models used from variance and covariance matrix. For this purpose, we use a linear transformation which is obtained from the Cholesky decomposition of covariance matrix and eliminate linear correlation among financial assets. In the following, variance is an appropriate criterion for the risk when distribution of stock returns is to be normal and symmetric as such a thing does not occur in reality. On the other hand, investors of the financial markets do not have an equal reaction to positive and negative exchanges of the stocks and show more desirability towards the positive exchanges and higher sensitivity to the negative exchanges. Therefore, we present a diversification model in the mean-semivariance framework which is based on the desirability or sensitivity of investor to positive and negative exchanges, and rate of this desirability or sensitivity can be controlled by use of a coefficient.

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