Risk tolerance and optimal portfolio choice

Similar documents
General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

The Global Trade and Environment Model: GTEM

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

KINEMATICS OF RIGID BODIES

A STOCHASTIC MODELING FOR THE UNSTABLE FINANCIAL MARKETS

[ ] 0. = (2) = a q dimensional vector of observable instrumental variables that are in the information set m constituents of u

Kalman Filter: an instance of Bayes Filter. Kalman Filter: an instance of Bayes Filter. Kalman Filter. Linear dynamics with Gaussian noise

r P + '% 2 r v(r) End pressures P 1 (high) and P 2 (low) P 1 , which must be independent of z, so # dz dz = P 2 " P 1 = " #P L L,

Reinforcement learning

MEEN 617 Handout #11 MODAL ANALYSIS OF MDOF Systems with VISCOUS DAMPING

Optimal Investment Strategy Insurance Company

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions

Servomechanism Design

International Journal of Pure and Applied Sciences and Technology

On Control Problem Described by Infinite System of First-Order Differential Equations

Lecture 22 Electromagnetic Waves

The sudden release of a large amount of energy E into a background fluid of density

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION

The Production of Polarization

7 Wave Equation in Higher Dimensions

Two-dimensional Effects on the CSR Interaction Forces for an Energy-Chirped Bunch. Rui Li, J. Bisognano, R. Legg, and R. Bosch

Exponential and Logarithmic Equations and Properties of Logarithms. Properties. Properties. log. Exponential. Logarithmic.

Variance and Covariance Processes

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

Examples of Dynamic Programming Problems

Projection of geometric models

CS 188: Artificial Intelligence Fall Probabilistic Models

Orthotropic Materials

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t

EMS SCM joint meeting. On stochastic partial differential equations of parabolic type

1. Consider a pure-exchange economy with stochastic endowments. The state of the economy

Finite-Sample Effects on the Standardized Returns of the Tokyo Stock Exchange

On the Semi-Discrete Davey-Stewartson System with Self-Consistent Sources

P h y s i c s F a c t s h e e t

Evaluating the Economic Impacts of a Disaster: A CGE Application to the Tokai Region of Japan

MATHEMATICAL FOUNDATIONS FOR APPROXIMATING PARTICLE BEHAVIOUR AT RADIUS OF THE PLANCK LENGTH

Lecture 17: Kinetics of Phase Growth in a Two-component System:

f(s)dw Solution 1. Approximate f by piece-wise constant left-continuous non-random functions f n such that (f(s) f n (s)) 2 ds 0.

Cooperative Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS. August 8, :45 a.m. to 1:00 p.m.

156 There are 9 books stacked on a shelf. The thickness of each book is either 1 inch or 2

On the local convexity of the implied volatility curve in uncorrelated stochastic volatility models

Authors name Giuliano Bettini* Alberto Bicci** Title Equivalent waveguide representation for Dirac plane waves

Monochromatic Wave over One and Two Bars

An Automatic Door Sensor Using Image Processing

Essential Microeconomics : OPTIMAL CONTROL 1. Consider the following class of optimization problems

A Learning Model of Dividend Smoothing

Deviation probability bounds for fractional martingales and related remarks

Fig. 1S. The antenna construction: (a) main geometrical parameters, (b) the wire support pillar and (c) the console link between wire and coaxial

The Valuation of Greenhouse Gas (GHG) Emissions Allowances

On The Estimation of Two Missing Values in Randomized Complete Block Designs

AN EVOLUTIONARY APPROACH FOR SOLVING DIFFERENTIAL EQUATIONS

Optimal Long-term Contracting with Learning

Lecture 20: Riccati Equations and Least Squares Feedback Control

(MS, ) Problem 1

The k-filtering Applied to Wave Electric and Magnetic Field Measurements from Cluster

PARAMETER IDENTIFICATION IN DYNAMIC ECONOMIC MODELS*

SZG Macro 2011 Lecture 3: Dynamic Programming. SZG macro 2011 lecture 3 1

Utility maximization in incomplete markets

Modelling Dynamic Conditional Correlations in the Volatility of Spot and Forward Oil Price Returns

336 ERIDANI kfk Lp = sup jf(y) ; f () jj j p p whee he supemum is aken ove all open balls = (a ) inr n, jj is he Lebesgue measue of in R n, () =(), f

Macroeconomic Theory Ph.D. Qualifying Examination Fall 2005 ANSWER EACH PART IN A SEPARATE BLUE BOOK. PART ONE: ANSWER IN BOOK 1 WEIGHT 1/3

Gauge invariance and the vacuum state. Dan Solomon Rauland-Borg Corporation 3450 W. Oakton Skokie, IL Please send all correspondence to:

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance.

Control Volume Derivation

Learning in Dynamic Incentive Contracts

EVENT HORIZONS IN COSMOLOGY

Chapter Finite Difference Method for Ordinary Differential Equations

Solutions Problem Set 3 Macro II (14.452)

BU Macro BU Macro Fall 2008, Lecture 4

Measures the linear dependence or the correlation between r t and r t-p. (summarizes serial dependence)

Reinforcement learning

( ) exp i ω b ( ) [ III-1 ] exp( i ω ab. exp( i ω ba

Cash Flow Valuation Mode Lin Discrete Time

A Weighted Moving Average Process for Forecasting. Shou Hsing Shih Chris P. Tsokos

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still.

Economics 8105 Macroeconomic Theory Recitation 6

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes]

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

b g [2] M N QP L N M

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims

Existence and uniqueness of solution for multidimensional BSDE with local conditions on the coefficient

Circular Motion. Radians. One revolution is equivalent to which is also equivalent to 2π radians. Therefore we can.

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n

Risk Aversion Asymptotics for Power Utility Maximization

Lag synchronization of hyperchaotic complex nonlinear systems via passive control

A general continuous auction system in presence of insiders

Problem Set on Differential Equations

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 6 SECTION 6.1: LIFE CYCLE CONSUMPTION AND WEALTH T 1. . Let ct. ) is a strictly concave function of c

Dual Hierarchies of a Multi-Component Camassa Holm System

Optimal Investment, Consumption and Retirement Decision with Disutility and Borrowing Constraints

The Brock-Mirman Stochastic Growth Model

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

This document was generated at 7:34 PM, 07/27/09 Copyright 2009 Richard T. Woodward

Sharif University of Technology - CEDRA By: Professor Ali Meghdari

Optimal Consumption and Investment Portfolio in Jump markets. Optimal Consumption and Portfolio of Investment in a Financial Market with Jumps

Generalized Snell envelope and BSDE With Two general Reflecting Barriers

The Optimal Stopping Time for Selling an Asset When It Is Uncertain Whether the Price Process Is Increasing or Decreasing When the Horizon Is Infinite

Electromagnetic Stealth with Parallel electric and magnetic Fields

Transcription:

Risk oleance and opimal pofolio choice Maek Musiela BNP Paibas London Copoae and Invesmen

Join wok wih T. Zaiphopoulou (UT usin) Invesmens and fowad uiliies Pepin 6 Backwad and fowad dynamic uiliies and hei associaed picing sysems: Case sudy of he binomial model Indiffeence picing PUP (5) Invesmen and valuaion unde backwad and fowad dynamic uiliies in a sochasic faco model o appea in Dilip Madan s Fesschif (6) Copoae and Invesmen

Conens Invesmen banking and maingale heoy Invesmen banking and uiliy heoy Main weaknesses Dynamic uiliy Value funcion and dynamic uiliy lenaive appoach Opimal pofolio Pofolio dynamics Eplici soluion Eamples Conclusions Copoae and Invesmen 3

Invesmen banking and maingale heoy Ideal elaionship Mahemaical logic of he deivaive business pefecly in line wih he heoy Picing by eplicaion comes down o calculaion of an epecaion wih espec o a maingale measue Issues of he measue choice and model specificaion and implemenaion deal wih by he appopiae eseves policy Howeve he moden invesmen banking is no abou hedging (he essence of picing by eplicaion) Indeed i is much moe abou eun on capial - he business of hedging offes he lowes eun Copoae and Invesmen 4

Invesmen banking and uiliy heoy Dysfuncional elaionship Mahemaical uiliy heoy fomulaed in a vey absac way and focused on solving poblems of limied pacical impoance Economic uiliy heoy fomulaed and developed in he cone which is no diecly focused on applicaions in invesmen banking When efomulaed in he invesmen cone i faces he difficuly o eplain he inuiive meaning of uiliy Only vey spoadic eamples whee uiliy was used in a picing cone To he bes of my knowledge eemely limied use in he asse allocaion cone Copoae and Invesmen 5

Main weaknesses No clea idea how o specify he uiliy funcion The classical o ecusive uiliy is defined in isolaion o he invesmen oppouniies given o an agen Eplici soluions o he opimal invesmen poblems can only be deived unde vey esicive model and uiliy assumpions - dependence on he Makovian assumpion and HJB equaions The geneal non Makovian models concenae on he mahemaical quesions of eisence of opimal allocaions and on he dual epesenaion of uiliy No easy way o develop pacical inuiion fo he asse allocaion Copoae and Invesmen 6

Dynamic uiliy U() is an adaped pocess s a funcion of U is inceasing and concave Fo each self-financing saegy he associaed (discouned) wealh saisfies E P ( ( π ) ) ( π U X F U X s) s s s Thee eiss a self-financing saegy fo which he associaed (discouned) wealh saisfies E P ( ( ) ) ( ) π π U X F U X s s s s Copoae and Invesmen 7

Value funcion and dynamic uiliy Value funcion V ( ) ( π sup E u X T) P ( ) π F X T π T Dynamic pogamming pinciple V ( ) ( ( ) ) π π X s E V X F s T s P s Dynamic uiliy coincides wih he value funcion U ( ) V ( ) R T Copoae and Invesmen 8

Difficulies Dynamic uiliy U() is defined by specifying he uiliy funcion u(t) and hen calculaing he value funcion The uiliy a ime i.e. U() may be vey complicaed and quie uninuiive. I depends songly on he specificaion of he make dynamics The analysis of such uiliies depends songly on he Makovian assumpion fo he asse dynamics and he use of HJB equaions Only vey specific cases of such uiliies like eponenial can be analysed in a model independen way Copoae and Invesmen 9

lenaive appoach an eample Sa by defining he uiliy funcion a ime i.e. se U()u() Define an adapive pocess U() by combining he vaiaional and he make elaed inpus o saisfy he popeies of a dynamic uiliy Benefis The funcion u() epesens he uiliy fo oday and no fo say en yeas ahead The vaiaional inpus ae he same fo he geneal classes of make dynamics no Makovian assumpion equied The make inpus have diec inuiive inepeaion The family of such uiliies is sufficienly ich o allow fo hinking abou allocaions in a way which is model and uiliy independen Copoae and Invesmen

Vaiaional inpus Uiliy equaion u u u Risk oleance equaion ( ) u u ( ) ( ) Copoae and Invesmen

Make inpus Invesmen univese of one iskless and k isky secuiies Geneal Io ype dynamics fo he isky secuiies Sandad d-dimensional Bownian moion diving he dynamics of he aded asses Taded asses dynamics ds db i ( i i µ d σ dw ) i S i... k B d Copoae and Invesmen

Make inpus Using mai and veco noaion assume eisence of he make pice fo isk pocess which saisfies Benchmak pocess d Views (consains) pocess Subodinaion pocess µ σ T ( d dw ) σσ δ δ δ dz Z φ dw Z d σ σ ( φ ) δ d Copoae and Invesmen 3

4 Copoae and Invesmen lenaive appoach an eample Unde he above assumpions he pocess U() defined below is a dynamic uiliy I uns ou ha fo a given self-financing saegy geneaing wealh X one can wie ( ) Z u U ( ) ( ) X R d R R X Z u dw U XZ u Z u X du du φ σσ δ σπ φ δ σπ

5 Copoae and Invesmen Opimal pofolio The opimal pofolio is given by Obseve ha The opimal wealh he associaed isk oleance and he opimal allocaions ae benchmaked The opimal pofolio incopoaes he inveso views o consains on op of he make equilibium The opimal pofolio depends on he inveso isk oleance a ime. ( ) ( ) ( ) X R R R X φ δ σ π

6 Copoae and Invesmen Pofolio dynamics ssume ha he following pocesses ae coninuous veco-valued semimaingales Then he opimal pofolio uns ou o be a coninuous veco-valued semimaingale as well. Indeed φ σ σ δ σ ( ) R R X φ σ σ δ σ π

Wealh and isk oleance dynamics The dynamics of he (benchmaked) opimal wealh and isk oleance ae given by d X R dr X ( σ σ ( φ ) δ ) ( ( δ ) d dw ) d X Obseve ha eo isk oleance anslaes o following he benchmak and geneaing pue bea eposue. In wha follows we assume ha he funcion () is sicly posiive fo all and Copoae and Invesmen 7

Canonical vaiables The wealh and isk oleance dynamics can be wien as follows d dm X Obseve ha ( σ σ ( φ ) δ ) (( δ ) d dw ) R dm dr d M d X dr dm Inoduce he pocesses Copoae and Invesmen X ( ) () ( ) () R ( ) w() M ( ) 8

Canonical dynamics The pevious sysem of equaions becomes d d () () dw () ( ) () ( () ) () dw () ( ) ( ) y I uns ou ha i can be solved analyically Copoae and Invesmen 9

Linea equaion Le h() be he invese funcion of ( u ) du h ( ) ( u ) du ( ) I uns ou ha h() solves he following linea equaion h h ( ) h ( ) ( u ) du h ( ) Copoae and Invesmen

Eplici epesenaion Soluion o he sysem of equaions is given by ( ) h( ( ) ) () h ( () ) ( () ) h ( ) ( s ) ds w( ) One can easily eve o he oiginal coodinaes and obain he eplici epessions fo X R Copoae and Invesmen

Copoae and Invesmen Opimal wealh The opimal (benchmaked) wealh can be wien as follows ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) d M d d dw d dm M d h du u h h X s s δ φ σ σ δ δ φ σ σ

3 Copoae and Invesmen Risk oleance The isk oleance pocess can be wien as follows ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) d M d d dw d dm M d h du u h h R s s δ φ σ σ δ δ φ σ σ

Bea and alpha Fo an abiay isk oleance he inveso will geneae pue bea by fomulaing he appopiae views on op of make equilibium indeed σ σ ( φ ) δ d dr X To geneae some alpha on op of he bea he inveso needs o oleae some isk bu may also fomulae views on op of make equilibium Copoae and Invesmen 4

No benchmak and no views The opimal allocaions given below ae epessed in he discouned wih he iskless asse amouns π R σ d σ σ d ( X ) ( ) ( ) They depend on he make pice of isk asse volailiies and he inveso s isk oleance a ime. R Copoae and Invesmen Obseve no diec dependence on he uiliy funcion and he link beween he disibuion of he opimal (discouned) wealh in he fuue and he implici o i cuen isk oleance of he inveso 5

No benchmak and hedging consain The deivaives business can be seen fom he invesmen pespecive as an aciviy fo which i is opimal o hold a pofolio which eans iskless ae By fomulaing views agains make equilibium one akes a isk neual posiion and allocaes eo wealh o he isky invesmen. Indeed δ φ π Ohe consains can also be incopoaed by he appopiae specificaion of he benchmak and of he veco of views Copoae and Invesmen 6

7 Copoae and Invesmen No iskless allocaion Take a veco such ha Define The opimal allocaion is given by I pus eo wealh ino he iskless asse. Indeed σ ν ( ) φ σ σ δ ν ν σ σ φ ( ) X φ σ π X X ν ν σ σ σ π

Seps o follow Specify he invesmen univese and is equilibium dynamics Deemine he cuen isk oleance of an inveso elaively o ha univese (could y o imply i fom he specificaion of fuue wealh disibuion) Specify a benchmak and views o consains Solve he FDE o ecove he funcion () Deemine he vaiaional inpu u() of he uiliy funcion namely solve y uu u u( ) ep ddy ( ) We se he uiliy of eo wealh a ime eo o be eo and he slope of he uiliy a ime eo fo eo wealh o be equal o one. Of couse ohe choices ae possible Copoae and Invesmen 8

Seps o follow Specify he dynamic fowad uiliy by combining he vaiaional inpu wih he choice of a benchmak views o consains The opimal pofolio is opimal wih espec o his uiliy Recove he funcion h() which is he invese of he funcion du ( u ) Specify he opimal wealh and isk oleance pocesses nalyse he oucome and poenially ecalibae Copoae and Invesmen 9

Disclaime The views epessed in his pesenaion ae hose of he auho and no necessaily hose of BNP Paibas Copoae and Invesmen 3