Guest Presentation
Upcoming SlideShare
Loading in...5
×
 

Guest Presentation

on

  • 476 views

 

Statistics

Views

Total Views
476
Views on SlideShare
476
Embed Views
0

Actions

Likes
0
Downloads
1
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Guest Presentation Guest Presentation Presentation Transcript

  • Investment Performance Measurement, Risk Tolerance and Optimal Portfolio Choice Marek Musiela, BNP Paribas, London
  • Joint work with T. Zariphopoulou (UT Austin)
      • Investments and forward utilities, Preprint 2006
      • Backward and forward dynamic utilities and their associated pricing systems: Case study of the binomial model, Indifference pricing, PUP (2005)
      • Investment and valuation under backward and forward dynamic utilities in a stochastic factor model, to appear in Dilip Madan’s Festschrift (2006)
      • Investment performance measurement, risk tolerance and optimal portfolio choice, Preprint 2007
  • Contents
    • Section 1 Investment banking and martingale theory
    • Section 2 Investment banking and utility theory
    • Section 3 The classical formulation
    • Section 4 Remarks
    • Section 5 Dynamic utility
    • Section 6 Example – value function
    • Section 7 Weaknesses of such specification
    • Section 8 Alternative approach
    • Section 9 Optimal portfolio
    • Section 10 Portfolio dynamics
    View slide
  • Investment banking and martingale theory
      • Mathematical logic of the derivative business perfectly in line with the theory
      • Pricing by replication comes down to calculation of an expectation with respect to a martingale measure
      • Issues of the measure choice and model specification and implementation dealt with by the appropriate reserves policy
      • However, the modern investment banking is not about hedging (the essence of pricing by replication)
      • Indeed, it is much more about return on capital - the business of hedging offers the lowest return
    View slide
  • Investment banking and utility theory
      • No clear idea how to specify the utility function
      • The classical or recursive utility is defined in isolation to the investment opportunities given to an agent
      • Explicit solutions to the optimal investment problems can only be derived under very restrictive model and utility assumptions - dependence on the Markovian assumption and HJB equations
      • The general non Markovian models concentrate on the mathematical questions of existence of optimal allocations and on the dual representation of utility
      • No easy way to develop practical intuition for the asset allocation
      • Creates potential intertemporal inconsistency
  • The classical formulation
      • Choose a utility function, say U(x), for a fixed investment horizon T
      • Specify the investment universe, i.e., the dynamics of assets which can be traded
      • Solve for a self financing strategy which maximizes the expected utility of terminal wealth
      • Shortcomings
        • The investor may want to use intertemporal diversification, i.e., implement short, medium and long term strategies
        • Can the same utility be used for all time horizons?
        • No – in fact the investor gets more value (in terms of the value function) from a longer term investment
        • At the optimum the investor should become indifferent to the investment horizon
  • Remarks
      • In the classical formulation the utility refers to the utility for the last rebalancing period
      • There is a need to define utility (or the investment performance criteria) for any intermediate rebalancing period
      • This needs to be done in a way which maintains the intertemporal consistency
      • For this at the optimum the investor must be indifferent to the investment horizon
      • Only at the optimum the investor achieves on the average his performance objectives
      • Sub optimally he experiences decreasing future expected performance
  • Dynamic performance process
      • U(x,t) is an adapted process
      • As a function of x, U is increasing and concave
      • For each self-financing strategy the associated (discounted) wealth satisfies
      • There exists a self-financing strategy for which the associated (discounted) wealth satisfies
  • Example - value function
      • Value function
      • Dynamic programming principle
      • Value function defines dynamic a performance process
  • Weaknesses of such specification
      • Dynamic performance process U(x,t) is defined by specifying the utility function u(x,T) and then calculating the value function
      • At time 0, U(x,0) may be very complicated and quite unintuitive
      • It depends strongly on the specification of the market dynamics
      • The analysis of such processes requires Markovian assumption for the asset dynamics and the use of HJB equations
      • Only very specific cases, like exponential, can be analysed in a model independent way
  • Alternative approach – an example
      • Start by defining the utility function at time 0, i.e., set U(x,0)=u(x,0)
      • Define an adapted process U(x,t) by combining the variational and the market related inputs to satisfy the properties of a dynamic performance process
      • Benefits
        • The function u(x,0) represents the utility for today and not for, say, ten years ahead
        • The variational inputs are the same for the general classes of market dynamics – no Markovian assumption required
        • The market inputs have direct intuitive interpretation
        • The family of such processes is sufficiently rich to allow for analysis optimal allocations in ways which are model and preference choice independent
  • Differential inputs
      • Utility equation
      • Risk tolerance equation
  • Market inputs
      • Investment universe of 1 riskless and k risky securities
      • General Ito type dynamics for the risky securities
      • Standard d-dimensional Brownian motion driving the dynamics of the traded assets
      • Traded assets dynamics
  • Market inputs
      • Using matrix and vector notation assume existence of the market price for risk process which satisfies
      • Benchmark process
      • Views (constraints) process
      • Time rescaling process
  • Alternative approach – an example
      • Under the above assumptions the process U(x,t), defined below is a dynamic performance
      • It turns out that for a given self-financing strategy generating wealth X one can write
  • Optimal portfolio
      • The optimal portfolio is given by
      • Observe that
        • The optimal wealth, the associated risk tolerance and the optimal allocations are benchmarked
        • The optimal portfolio incorporates the investor views or constraints on top of the market equilibrium
        • The optimal portfolio depends on the investor risk tolerance at time 0.
  • Wealth and risk tolerance dynamics
      • The dynamics of the (benchmarked) optimal wealth and risk tolerance are given by
      • Observe that zero risk tolerance translates to following the benchmark and generating pure beta exposure.
      • In what follows we assume that the function r(x,t) is strictly positive for all x and t
  • Beta and alpha
      • For an arbitrary risk tolerance the investor will generate pure beta by formulating the appropriate views on top of market equilibrium, indeed,
      • To generate some alpha on top of the beta the investor needs to tolerate some risk but may also formulate views on top of market equilibrium
  • No benchmark and no views
      • The optimal allocations, given below, are expressed in the discounted with the riskless asset amounts
      • They depend on the market price of risk, asset volatilities and the investor’s risk tolerance at time 0.
      • Observe no direct dependence on the utility function, and the link between the distribution of the optimal (discounted) wealth in the future and the implicit to it current risk tolerance of the investor
  • No benchmark and hedging constraint
      • The derivatives business can be seen from the investment perspective as an activity for which it is optimal to hold a portfolio which earns riskless rate
      • By formulating views against market equilibrium, one takes a risk neutral position and allocates zero wealth to the risky investment. Indeed,
      • Other constraints can also be incorporated by the appropriate specification of the benchmark and of the vector of views
  • No riskless allocation
      • Take a vector such that
      • Define
      • The optimal allocation is given by
      • It puts zero wealth into the riskless asset. Indeed,
  • Space time harmonic functions
      • Assume that h(z,t) is positive and satisfies
      • Then there exists a positive random variable H such that
      • Non-positive solutions are differences of positive solutions
  • Risk tolerance function
      • Take an increasing space time harmonic function h(z,t)
      • Define the risk tolerance function r(z,t) by
      • It turns out that r(z,t) satisfies the risk tolerance equation
  • Example
      • For positive constants a and b define
      • Observe that
      • The corresponding u(z,t) function can be calculated explicitly
      • The above class covers the classical exponential, logarithmic and power cases