Optimizers everywhere - Optimization in Financial Applications … › content › dam › mathworks...
Transcript of Optimizers everywhere - Optimization in Financial Applications … › content › dam › mathworks...
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1© 2016 The MathWorks, Inc.
Optimizers everywhere -
Optimization in Financial Applications with MATLAB
Dr. Sarah DrewesMathWorks Consulting Services
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Optimization
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Optimization in Financial Applications with MATLAB
§ Financial Optimization
§ Optimization Methods
§ Customized optimization models
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Financial Optimization
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Financial Applications and Optimization
Asset Management
Trading
Risk Management
Portfolio Optimization
Machine Learning
Regression
Maximum Likelihood Estimation
Distribution Fitting
Curve Fitting
ALM
Econometrics
Pricing & Valuation
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MATLAB – The Financial Development Platform
Financial
Statistics Optimization
Financial Instruments Econometrics
MATLAB
Parallel Computing MATLAB Distributed Computing Server
Files
Databases
Datafeeds
AccessReporting
Applications
Production
ShareData Analysis and Visualization
Financial Modeling
Application Development
Research and Quantify
Datafeed
Database
Spreadsheet Link EX
MATLAB Compiler
SDK
MATLAB Compiler
Report Generator
Production Server
Trading Toolbox
Neural Network Toolbox
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– Mean-Variance Portfolio Optimization– Conditional Value-at-Risk Portfolio Optimization– Mean-Absolute Deviation Portfolios
– Time Series Regression Models– Conditional Mean Variance Models– Multivariate Models
– Linear/ Nonlinear Regression– Probability disribution fitting– Machine Learning, e.g., SVM, NN,...
– Nonlinear Regression, Convolutional NeuralNetworks
Financial Optimization within MATLAB
Financial
Statistics
Econometrics
Neural Network
Optimization
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Optimization Methods
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OptimizationProblem
ObjectiveFunction
uxl
eqbxeqA
bAx
≤≤
=
≤
)(min xfx
Typicallyalinearornonlinearfunction
Linearconstraints- inequalities- equalities- bounds
Nonlinearconstraints- inequalities- equalities
Decisionvariables(canbediscreteorinteger)
0)(
0)(
=
≤
xeqc
xc
SubjecttoConstraints
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How to solve an optimization problem ?
What do you know about your optimization problem ?
information
accuracy
information
runtime
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Variables & Constraints
VariablesContinuous Discrete
0 0.2 0.4 0.6 0.8 10
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0 5 10 15 200
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Integer Programming
00.2
0.40.6
0.81
00.2
0.4
0.60.8
1-1
-0.5
0
0.5
1
1.5
2
2.5
3
ConstraintsLinear Nonlinear
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00.2
0.40.6
0.81
00.2
0.4
0.60.8
1-1
-0.5
0
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1.5
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00.2
0.40.6
0.81
00.2
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0.60.8
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0.1
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-2-1
01
2
-2
-1
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Objective Function
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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Least-squares/curve fitting
Quadratic f(x)=xTAxLinear f(x)=ATx
General f(x)
f(c)=Σ[g(xk;c)– yk]2
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Numerical optimization
xxxfxxfxf
ΔΔ−−Δ+
≈ʹ2
)()()(
function [f,df] = objective(x)f = ... % function valuedf = ... % gradient vector
ü Fewer function evaluationsü More accurate
Whenever possible, provide gradient/hessian information!
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Derivative-Free Optimization
?=∇f fmincon
Repeatedly sampleseveral pointsDirect Search
Genetic Algorithm
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ApproachesinMATLAB
§ LocalOptimization– Findslocalminima/maxima- Usessuppliedgradientsorestimatesthem- Applicable forlargescaleproblemswith
smoothobjectivefunction– Faster/fewerfunctionevaluations
§ GlobalOptimization– Nogradientinformationrequired– Solveproblemswithnon-smooth,
discontinuousobjective function
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Solvers
Variables
Continuous0 0.2 0.4 0.6 0.8 10
10
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0 5 10 15 200
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00.2
0.40.6
0.81
00.2
0.4
0.60.8
1-1
-0.5
0
0.5
1
1.5
2
2.5
3
Constraints
Linear
NonlinearDiscrete
00.2
0.40.6
0.81
00.2
0.4
0.60.8
1-1
-0.5
0
0.5
1
1.5
2
2.5
3
00.2
0.40.6
0.81
00.2
0.4
0.60.8
10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-2-1
01
2
-2
-1
0
1
2-8
-6
-4
-2
0
2
4
6
8
10
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
20
40
60
80
100
120 Least-squares
Quadratic f(x)=xTAx
Linear f(x)=ATx
General f(x)
f(c)=Σ[g(xk;c)– yk]2
Objective function Solver
linprog
quadprog
intlinprog
lsqlin
lsqnonlin
fminunc
fmincon
Optimization Toolbox
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Solvers
Variables
Continuous
0 0.2 0.4 0.6 0.8 10
10
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40
50
60
70
80
90
100
0 5 10 15 200
10
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Constraints
Discrete
Nonlinear f(x)
Objective function Solver
GlobalSearch,MultiStart
Non-smooth patternsearch,ga,simulannealbnd
ga
Global Optimization Toolbox
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Customized Optimization Models
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Supported Portfolio Optimization Models
§ Mean-Variance Portfolio Optimization
§ Conditional Value-at-Risk Portfolio Optimization
§ Mean-Absolute Deviation Portfolio Optimization
Financial Toolbox
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Customized Objective - Smart Beta
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Customized Portfolio Optimization -Smart Beta
Smart Beta Example – Risk Parity:
Generate a portfolio where each asset’s marginal contribution to risk is equal
§ Marginal Contributions for N assets
§ Minimize distance between all contributions 𝑓 𝑥 =$$|𝑀𝐶( 𝑥 −𝑀𝐶) 𝑥 |)*(
+
(,-
𝑀𝐶( 𝑥 = 𝑥(×∑ 𝑥)𝐶𝑜𝑣(𝑅(,𝑅))+),-
𝜎
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Customized Portfolio Optimization -Smart Beta
§ Create marginal risk contributions
§ Create a distance matrix
§ Minimize the total distance between all the contributions
𝑓 𝑥 =$$|𝑀𝐶( 𝑥 −𝑀𝐶) 𝑥 |)*(
+
(,-
𝑀𝐶( 𝑥 = 𝑥(×∑ 𝑥)𝐶𝑜𝑣(𝑅(,𝑅))+),-
𝜎
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Customized Portfolio Optimization -Smart Beta
Smart Beta Example – Risk Parity:
𝑚𝑖𝑛;
𝑓 𝑥 ≔$$|𝑀𝐶( 𝑥 −𝑀𝐶) 𝑥 |)*(
+
(,-
s.t. 𝑥=e = 1lb ≤ x ≤ ub
à Nonlinear objective function with linear equality and bound constraints
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Customized Portfolio Optimization -Smart Beta
§ Solve with fmincon from MATLAB® Optimization Toolbox™
totaldistancebetweenmarginalriskcontributions
budgetconstraint
bounds
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Customized Portfolio Optimization -Smart Beta
Assets
Time
Marginal RiskContribution
Time
Portfolio Risk
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Customized Constraints - Robust Constraints
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Customized Portfolio Optimization -Robust Constraints
Mean-Variance Portfolio Optimizationmin;𝑥=𝐶𝑥
𝑠. 𝑡. 𝑚= 𝑥 ≥ 𝑅𝑥=𝑒 = 1
x ≥ 0
Covariancematrix
meanofassetreturns
Financial Toolbox
lower bound
budget constraint
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Customized Portfolio Optimization -Robust Constraints
Additional constraints supported by
§ Linear inequality constraints§ Linear equality constraints§ Group constraints§ Group ratio constraints§ Average turnover constraints§ One-way turnover constraints
Financial Toolbox
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Extend standard model by individual constraints, e.g. robust constraints
𝑃𝑟𝑜𝑏(𝜉=x≥ 𝑅)≥ p
àDeterministic approximation (Chebychev’s inequality)
P. Bonami, M.A. Lejeune, ‚An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints’, Operations Research 2009, Vol. 57,Issue 3
Customized Portfolio Optimization-Robust Constraints
randomvectorofreturns
probabilitylevel
𝑚=𝑥 − T-UT
𝑥=𝐶𝑥 ≥ 𝑅
returnlevel
meanvectorofreturns
covarianceofreturns
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Customized Portfolio Optimization -Robust Constraints
Mean-Variance Portfolio Optimization with robust constraint
min;𝑓 𝑥 ≔ 𝑥=𝐶𝑥
𝑠. 𝑡𝑥=𝑒 = 1x ≥ 0 lower bound
budget constraint
𝑚=𝑥 − T-UT 𝑥=𝐶𝑥 ≥ 𝑅
robust constraint
à Robust mean variance model is a nonlinear, convex NLP
𝑐 𝑥 ≔ 𝑅 −𝑚= 𝑥 +𝑝
1 − 𝑝𝑥=𝐶𝑥 ≤ 0
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Customized Portfolio Optimization-Robust Constraints
§ Solve with fmincon from MATLAB® Optimization Toolbox™
mean-varianceriskproxy
budgetconstraint
robustconstraint
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Customized Portfolio Optimization-Robust Constraints
Provide gradients
𝛻𝑓 𝑥𝛻𝑐 𝑥
𝑓 𝑥𝑐 𝑥
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Customized Portfolio Optimization-Robust Constraints
Provide Hessian
𝛻Z𝑓 𝑥 +$𝛻Z𝑐( 𝑥 [ 𝜆( +$𝛻Z𝑐𝑒𝑞) 𝑥 [ 𝜆)
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Customized Portfolio Optimization -Robust Constraints
information
accuracy
information
runtime
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Customized Portfolio Optimization -Robust Constraints
Without gradients With gradients
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Customized Portfolio Optimization -Robust Constraints
Without gradients With gradients
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Customized Portfolio Optimization -Robust Constraints
R
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Customized Portfolio Optimization
R =
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MATLAB – The Financial Development Platform
Financial
Statistics Optimization
Financial Instruments Econometrics
MATLAB
Parallel Computing MATLAB Distributed Computing Server
Files
Databases
Datafeeds
AccessReporting
Applications
Production
ShareData Analysis and Visualization
Financial Modeling
Application Development
Research and Quantify
Datafeed
Database
Spreadsheet Link EX
MATLAB Compiler
SDK
MATLAB Compiler
Report Generator
Production Server
Trading Toolbox
Neural Network Toolbox
![Page 40: Optimizers everywhere - Optimization in Financial Applications … › content › dam › mathworks › math... · Discrete Nonlinear f(x) Objective function Solver GlobalSearch,](https://reader036.fdocuments.us/reader036/viewer/2022070822/5f28ef3725540372676e9be4/html5/thumbnails/40.jpg)
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Customized Portfolio Optimization -Deployment
§ Compile your MATLAB optimization model for your dedicated platform
§ Make it available for your enterprise environment
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Summary
§ Optimization for financial applications is built within MATLAB toolboxescovering many standard applications
§ A large variety of optimization algorithms available in MATLAB® Optimization Toolbox™ and Global Optimization Toolbox™
§ Customized optimization models made easy by quick modeling, advanced optimization process diagnostics and rapid deployment.
§ Enhance optimization performance and accuracy by adding maximal information.
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Thank you !