Ch 7 Modelling Investment Returns Flashcards
Modelling Investment returns, variance and correlations
Comment on small changes in inputs (portfolio weights very sensitive to input parameters)
- Portfolio weights are very sensitive to the input parameters … small changes in inputs can lead to large changes in outputs
- Studies have shown that portfolio weights are about :
11 x more sensitive to changes in forecast returns compared to volatility
20x more sensitive to changes in forecast returns compared to correlation - In order of importance then, forecast returns, then forecast variance, then forecast correlation
Modelling Investment returns, variance and correlations
Conservatism in modelling
- Equal returns, volatility and zero correlation … equally weighted portfolio
Conservatism in Modelling
Given correlation is the ‘least’ sensitive: equal returns, equal vol, small correlations (e.g. half of long term history) … will xxx portfolio vol due to xxx to risker assets
Given correlation is the ‘least’ sensitive: equal returns, equal vol, small correlations (e.g. half of long term history) … will UNDERESTIMATE portfolio vol due to an OVER ALLOCATION to risker assets
Conservatism in Modelling
Unequal returns case 1 : constant risk/return multiple …
Unequal returns case 1 : constant risk/return multiple … a sensible approach if you do not have confidence in your ability to distinguish between assets
Conservatism in Modelling
Unequal returns case 2 : if you believe long term history will repeat, ….
Unequal returns case 2 : if you believe long term history will repeat, then an application of shrinkage techniques to returns will assist
Conservatism in Modelling
Unequal variances: equal returns, volatility set sensibly (e.g. judicious choice of history (should GFC be in?), applied to all similar assets (e.g. all equities, all govt bonds, etc.) ), small correlations … will xxxx assets with xxxx volatility …
Unequal variances: equal returns, volatility set sensibly (e.g. judicious choice of history (should GFC be in?), applied to all similar assets (e.g. all equities, all govt bonds, etc.) ), small correlations … will OVERWEIGHT assets with LOW volatility … they will be a better risk/return trade off
How to squeeze extreme volatility and correlations towards the average
Use shrinkage and smoothing techniques (these help to minimise estimation errors
Mean reversion is generally evident in: (3)
Break?
- GDP Growth Rates
- FX rates
- Equities market
Can break - eg central bank intervention
Mean reversion
Be aware of the LEVEL of key return drivers
- eg equities
For equity markets valuation is critical – markets that are very expensive or very cheap will change direction at some point, often sharply … beware of a bubble building
Mean reversion - consider Momentum
- greed & fear … trends can continue for much longer than is warranted by economic fundamentals
- Consider serial correlation - there may be no justification for momentum but it could be worthwhikle factoring into models.
Long Term Expectations
Fixed Income
Real GDP Growth = xxx + yyy
Real GDP Growth = Population growth + Productivity growth
Long Term Expectations
Fixed Income
If Central Banks have neutral monetary policy and government budgets are balanced then
If Central Banks have neutral monetary policy and government budgets are balanced then
Short Term Risk Free Rate = Nominal GDP Growth
= Inflation + Real GDP Growth
Long Term Expectations
Fixed Income
Long Term Risk Free Rate = ??
Note there is no insight, but the historic premium has been…
Long Term Risk Free Rate = Short Term Risk Free Rate + 0.5% to 1% pa
Long term expectations - Fixed Income
Corporate Yield =
Corporate Yield = Risk Free Rate + Corporate Premium + Default Rate
Long term expectations - Fixed Income
Corporate Premiums:
1. Range
2. Default rate
Corporate Premiums
- have ranged between 70bps an 160bps in US, for A-grade bonds over government bonds. About 120bps seems reasonable
- Default rate: about ~12bps over the last 25 years