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Capital Market expectations: Forecasting asset class returns (Forecasting…
Capital Market expectations: Forecasting asset class returns
Forecasting Fixed income
Discount CFs/YTM
micro approach
Building block approach
Default rate (real + inflation risk) +
Term premium (duration risk) +
Credit premium (credit risk) +
Liquidity premium (liquidity risk)
Term premium drivers
level dependent inflation uncertainty
Ability to hedge recession risk (good hedge = low term premium)
Supply/demand of bonds at various maturities
Cyclical effects - shape of curve changes over the course of the business cycle
Credit premium
expected loss + risk of default or credit migration
credit premium captures spreads that default rate does not capture
credit spreads driven primary by credit premium + fin mkt conditions
IG = credit prem refelct credit migration risk
HY = cred prem reflect default risk = countercyclcial
Liquidity premium- as issues move away from these, premium increases
priced near par
relative new
from a large issuer
known issuer
structure
high quality
Risks in EM
1. Economic (ability to pay)
restriction on trade, capital flow
less educated, less skilled work force, limited infrastructure
reliance on foreign borrowing (hard currencies)
greater concentration of wealth and income
poor fiscal deficit
high debt/GDP
low GDP growth
2. Political & legal risk
weak property rights laws
weak enforcement of contract laws
sovereign immunity
Equities
more fragile economics
less stable political
weaker legal protections
Forecasting equity returns
1. Historical statistical approach
Shrinkage estimator (combining another estimate of the mean return with the sample mean, weighting one mean and combine)
2. DCF
Grinold-kroner model = expected CF return + expected nominal earnings growth + expected repricing return
= (D/p - % :small_red_triangle:shares oustanding) + % :small_red_triangle:E + % :small_red_triangle:P/E
3. Risk premium approach
ERP = historical equity prem are subject to estimation error
Singer & Terhaar (equilibrium approach) = combination of 2 CAPM models
asset class fully integrated and segmented (weighted)
add risk free rate to get E(r asset)
Forecasting Real estate returns
Historical returns
trade infrequently -> no price/return series
rely on appraisal data (price is smooth, which distorts volatility and correlations)
Real estate cycles
supply is fixed in short run, strong cyclical pattern to property values, rents, & occupancy rates
high quality, long leases -> low turnover, stable rents/occupancy
low quality-> sensitive to the economy, more volatile rents
1. Capitalization rates = NOI / property value
E(r) = Cap rate + NOI growth rate
Short run = NOI/p + g - %change in cap rate
pro-cyclical with interest rate (rates up, cap up)
counter-cyclical with credit spreads (rates up, cap rate down)
2. Risk premium
term prem
credit prem
equity risk prem
liquidity prem
3. Equilibrium framework
unsmoothed data series
equil models assume liquid assets
far less integrated
Forecasting FX rates
1. Goods & services, trade and competitiveness & sustainability current account
2. Capital flows
Current account = Capital account + FA
3. portfolio balance, portfolio composition & sustainability issues
Forecasting Volatility
Sample stat (estimate constant VCV-matrix)
VCV-matrix from multi-factor models = less correlations needed
Shrinkage estimation of VCV-matrix = combine both sample and factor model
Estimate vol from smooth returns
Current return = weight of true return x weight of past return
sample = variance of current return and past return
Time-varying vol: ARCH models