Financial Optimization Problems

2011 ◽  
pp. 75-144
Author(s):  
Nicole Bäuerle ◽  
Ulrich Rieder
Keyword(s):  
Optimization Problems ◽  
Financial Optimization

scholarly journals New Safe Approximation of Ambiguous Probabilistic Constraints for Financial Optimization Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
NingNing Du ◽  
Yan-Kui Liu ◽  
Ying Liu
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Perturbation ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Chinese Stock Market ◽  
Robust Solution ◽  
Return Rates ◽  
Financial Optimization

In financial optimization problem, the optimal portfolios usually depend heavily on the distributions of uncertain return rates. When the distributional information about uncertain return rates is partially available, it is important for investors to find a robust solution for immunization against the distribution uncertainty. The main contribution of this paper is to develop an ambiguous value-at-risk (VaR) optimization framework for portfolio selection problems, where the distributions of uncertain return rates are partially available. For tractability consideration, we deal with new safe approximations of ambiguous probabilistic constraints under two types of random perturbation sets and obtain two equivalent tractable formulations of the ambiguous probabilistic constraints. Finally, to demonstrate the potential for solving portfolio optimization problems, we provide a practical example about the Chinese stock market. The advantage of the proposed robust optimization method is also illustrated by comparing it with the existing optimization approach via numerical experiments.

CONFRONTING MODEL MISSPECIFICATION IN FINANCE: TRACTABLE COLLECTIONS OF SCENARIO PROBABILITY MEASURES FOR ROBUST FINANCIAL OPTIMIZATION PROBLEMS

2002 ◽  
Vol 05 (01) ◽  
pp. 33-54 ◽  
Author(s):  
CRAIG FRIEDMAN
Keyword(s):  
Risk Management ◽  
Asset Allocation ◽  
Financial Models ◽  
Optimization Problems ◽  
Model Misspecification ◽  
Asset Price ◽  
Probability Measures ◽  
Contingent Claim ◽  
Financial Optimization ◽  
Contingent Claim Pricing

Despite the widespread realization that financial models for contingent claim pricing, asset allocation and risk management depend critically on their underlying assumptions, the vast majority of financial models are based on single probability measures. In such models, asset prices are assumed to be random, but asset price probabilities are assumed to be known with certainty, an obviously false assumption. We explore practical methods to specify collections of probability measures for an assortment of important financial problems; we provide practical methods to solve the robust financial optimization problems that arise and, in the process, discover "dangerous" measures.

Scenario tree generation and multi-asset financial optimization problems

2013 ◽  
Vol 41 (5) ◽  
pp. 494-498 ◽  
Author(s):  
Alois Geyer ◽  
Michael Hanke ◽  
Alex Weissensteiner
Keyword(s):  
Optimization Problems ◽  
Scenario Tree ◽  
Financial Optimization ◽  
Tree Generation ◽  
Scenario Tree Generation

scholarly journals The price of conserving avian phylogenetic diversity: a global prioritization approach

2015 ◽  
Vol 370 (1662) ◽  
pp. 20140004 ◽  
Author(s):  
Laura A. Nunes ◽  
Samuel T. Turvey ◽  
James Rosindell
Keyword(s):  
Phylogenetic Diversity ◽  
Optimization Problems ◽  
Global Analysis ◽  
Cost Benefit ◽  
Biodiversity Loss ◽  
Current Conservation ◽  
Worst Case ◽  
Financial Optimization ◽  
Spending Patterns ◽  
Global Concern

The combination of rapid biodiversity loss and limited funds available for conservation represents a major global concern. While there are many approaches for conservation prioritization, few are framed as financial optimization problems. We use recently published avian data to conduct a global analysis of the financial resources required to conserve different quantities of phylogenetic diversity (PD). We introduce a new prioritization metric (ADEPD) that After Downlisting a species gives the Expected Phylogenetic Diversity at some future time. Unlike other metrics, ADEPD considers the benefits to future PD associated with downlisting a species (e.g. moving from Endangered to Vulnerable in the International Union for Conservation of Nature Red List). Combining ADEPD scores with data on the financial cost of downlisting different species provides a cost–benefit prioritization approach for conservation. We find that under worst-case spending $3915 can save 1 year of PD, while under optimal spending $1 can preserve over 16.7 years of PD. We find that current conservation spending patterns are only expected to preserve one quarter of the PD that optimal spending could achieve with the same total budget. Maximizing PD is only one approach within the wider goal of biodiversity conservation, but our analysis highlights more generally the danger involved in uninformed spending of limited resources.

Hidden Markov models for financial optimization problems

2009 ◽  
Vol 21 (2) ◽  
pp. 111-129 ◽  
Author(s):  
D. Roman ◽  
G. Mitra ◽  
N. Spagnolo
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Optimization Problems ◽  
Hidden Markov ◽  
Financial Optimization

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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