scholarly journals CVaR Robust Mean-CVaR Portfolio Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Maziar Salahi ◽  
Farshid Mehrdoust ◽  
Farzaneh Piri
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measure ◽  
Optimization Technique ◽  
Large Error ◽  
Data Uncertainty ◽  
Final Decision ◽  
Estimation Risk ◽  
Expected Return ◽  
The Impact

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio.

scholarly journals Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 692
Author(s):  
Clara Calvo ◽  
Carlos Ivorra ◽  
Vicente Liern ◽  
Blanca Pérez-Gladish
Keyword(s):  
Risk Measure ◽  
Statistical Information ◽  
Expected Returns ◽  
Financial Assets ◽  
Expected Return ◽  
Additional Information ◽  
Subjective Risk ◽  
The Impact ◽  
Statistical Risk ◽  
Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

scholarly journals Portfolio Optimization Constrained by Performance Attribution

2021 ◽  
Vol 14 (5) ◽  
pp. 201
Author(s):  
Yuan Hu ◽  
W. Brent Lindquist ◽  
Svetlozar T. Rachev
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Effect ◽  
Conditional Value At Risk ◽  
Positive Role ◽  
Performance Attribution ◽  
Dow Jones Industrial Average

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

scholarly journals LIQUIDITY RISK AND INSTABILITIES IN PORTFOLIO OPTIMIZATION

2016 ◽  
Vol 19 (05) ◽  
pp. 1650035 ◽  
Author(s):  
FABIO CACCIOLI ◽  
IMRE KONDOR ◽  
MATTEO MARSILI ◽  
SUSANNE STILL
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
Risk Measure ◽  
Estimation Error ◽  
Coherent Risk Measures ◽  
Important Special Case ◽  
Coherent Risk ◽  
Finite Samples ◽  
Impact Function ◽  
The Impact

We show that including a term which accounts for finite liquidity in portfolio optimization naturally mitigates the instabilities that arise in the estimation of coherent risk measures on finite samples. This is because taking into account the impact of trading in the market is mathematically equivalent to introducing a regularization on the risk measure. We show here that the impact function determines which regularizer is to be used. We also show that any regularizer based on the norm [Formula: see text] with [Formula: see text] makes the sensitivity of coherent risk measures to estimation error disappear, while regularizers with [Formula: see text] do not. The [Formula: see text] norm represents a border case: its “soft” implementation does not remove the instability, but rather shifts its locus, whereas its “hard” implementation (including hard limits or a ban on short selling) eliminates it. We demonstrate these effects on the important special case of expected shortfall (ES) which has recently become the global regulatory market risk measure.

Enhancing Portfolio Performance in Global Equity Allocation with a Forward-Looking Indicator

2018 ◽  
Keyword(s):  
Equilibrium State ◽  
Portfolio Optimization ◽  
Asset Allocation ◽  
Economic Freedom ◽  
Equity Markets ◽  
Portfolio Performance ◽  
Risk Minimization ◽  
Expected Return ◽  
Ex Post ◽  
Forward Looking

The Black–Litterman model provides a more reasonable platform for portfolio optimization and asset allocation, as compared to the traditional CAPM approach, by presenting an equilibrium state of the markets and only deviating from that equilibrium state with forward-looking strategic views. The Index of Economic Freedom (IEF) can be used as a handy tool for forming such strategic views on global markets. Ex-post performance analysis of portfolios covering both developed and developing equity markets constructed with CAPM, Black–Litterman equilibrium implied return, and Black–Litterman absolute view approaches shows that by smoothing expected return with changes in the IEF, significantly superior portfolio performance can be achieved at a lower risk. The Index of Economic Freedom contains superior information in terms of idiosyncratic country-specific risks, which the market seems to ignore or under price. This study has particular relevance to asset allocation strategy, portfolio optimization, and risk minimization in the context of global equity markets.

scholarly journals The Optimal Allocation for Capital Preservation: an Evidence Australian Portfolio

2018 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Riznaldi Akbar
Keyword(s):  
Asset Allocation ◽  
Optimal Allocation ◽  
Risk Measure ◽  
Alternative Measure ◽  
Annual Maximum ◽  
Expected Return ◽  
Risky Assets ◽  
Asset Class ◽  
Maximum Drawdown ◽  
Investor Preference

<p>This study analyzes optimal asset mix for Australian portfolios with the main investment objective for capital preservation. An alternative measure of risk of annual maximum drawdown has been used to reflect investor preference for capital preservation as opposed to conventional risk measure of standard deviation and variance. The contribution of the study is two folds. First, this study has put different perspective to look at portfolio risk in the view of capital preservation. Second, the optimal weight for asset class mix that minimizes annual maximum drawdown has been analyzed for the case of Australian market. The results suggest that for capital preservation, investors should expect lower returns and need to put a greater allocation on less risky assets such as cash or bond. To this end, cash and bond have provided stable long term annual returns along with contained level of annual maximum drawdowns. In contrast, when investors demand higher expected return, they should increase asset allocation into stocks (equities) market at the expense of higher maximum drawdowns.</p><p><strong>Bahasa Indonesia Abstrak</strong>: <em>Studi ini menganalisis bauran aset optimal untuk portofolio Australia dengan tujuan investasi utama untuk pelestarian modal. Ukuran alternatif risiko penarikan maksimum tahunan telah digunakan untuk mencerminkan preferensi investor untuk pelestarian modal dibandingkan dengan ukuran risiko konvensional standar deviasi dan varians. Kontribusi dari penelitian ini adalah dua lipatan. Pertama, penelitian ini telah menempatkan perspektif yang berbeda untuk melihat risiko portofolio dalam pandangan pelestarian modal. Kedua, bobot optimal untuk campuran kelas aset yang meminimalkan penarikan maksimum tahunan telah dianalisis untuk kasus pasar Australia. Hasilnya menunjukkan bahwa untuk pelestarian modal, investor harus mengharapkan pengembalian yang lebih rendah dan perlu menempatkan alokasi yang lebih besar pada aset yang kurang berisiko seperti uang tunai atau obligasi. Untuk tujuan ini, uang tunai dan obligasi telah memberikan pengembalian tahunan jangka panjang yang stabil bersama dengan tingkat penarikan maksimum tahunan. Sebaliknya, ketika investor meminta pengembalian yang diharapkan lebih tinggi, mereka harus meningkatkan alokasi aset ke pasar saham (ekuitas) dengan mengorbankan penarikan maksimum yang lebih tinggi</em></p>

scholarly journals THE FUTURE OF PASSENGER AIR TRANSPORT – VERY LARGE AIRCRAFT AND OUT KEY HUMAN FACTORS AFFECTING THE OPERATION AND SAFETY OF PASSENGER AIR TRANSPORT

2017 ◽  
Vol 12 ◽  
pp. 104
Author(s):  
Petra Skolilova
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Operating Cost ◽  
Aircraft Design ◽  
Air Transport ◽  
Final Decision ◽  
Factors Affecting ◽  
Fuel Burn ◽  
The Future ◽  
The Impact

The article outlines some human factors affecting the operation and safety of passenger air transport given the massive increase in the use of the VLA. Decrease of the impact of the CO2 world emissions is one of the key goals for the new aircraft design. The main wave is going to reduce the burned fuel. Therefore, the eco-efficiency engines combined with reasonable economic operation of the aircraft are very important from an aviation perspective. The prediction for the year 2030 says that about 90% of people, which will use long-haul flights to fly between big cities. So, the A380 was designed exactly for this time period, with a focus on the right capacity, right operating cost and right fuel burn per seat. There is no aircraft today with better fuel burn combined with eco-efficiency per seat, than the A380. The very large aircrafts (VLAs) are the future of the commercial passenger aviation. Operating cost versus safety or CO2 emissions versus increasing automation inside the new generation aircraft. Almost 80% of the world aircraft accidents are caused by human error based on wrong action, reaction or final decision of pilots, the catastrophic failures of aircraft systems, or air traffic control errors are not so frequent. So, we are at the beginning of a new age in passenger aviation and the role of the human factor is more important than ever.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

Sign in / Sign up

Export Citation Format

Share Document