Some stylized facts of Russian private pension funds
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Transcript of Some stylized facts of Russian private pension funds
Some stylized facts of Russian private pension funds
Didenko AlexanderInternational Financial Laboratory
Questions
• What funds are efficient?• What metrics to use?• Is there any persistence?• Do they inform customers about risks?• Do they have behavioral biases?
Dataset and methods
• 30 quarters * 30 private pension funds• IIIQ’ 05 – IVQ’ 12• Data Envelopment Analysis • Malmquist productivity index• T- and KS-tests• Granger causality
DEA - conceptual model
Input 1
Input 2
Input N
Production Plans
Output 1
Output 1
Output 1
Data envelopment analysis
• We have j DMUs• Which use v inputs x• To produce u outputs y• DEA-efficiency is
defined as a ratio of a weighted sum of outputs to a weighted sum of inputs
Example from Coopers et al.
Malmquist index
• Decomposition of dynamic DEA to three components:– technical efficiency change on the best practice
technologies – change in scale efficiency– technical change measured as a shift in the
benchmark technology – which sum to total change
DEA – general model for funds
Financial Capital
Risk
Human Capital
Pension Funds
Return
Market Share
DEA – our specificaion
CVaR
E+R Ratio
Pension Funds
Active return
NAV Share
Diversification
CVaR
• Wuertz, Chalabi, Chen, Ellis (2009);
• RUPAI, RUPCI, RGBI• Alpha=0.05• Weekly data• Average quarterly CVaR
Diversification
• There are plenty of D. measures• We use that of Goetzmann, Kumar, 2008
H1. Funds convey useful info in names
• “professionally-looking” terms to indicate attitude to risk– “Balanced”– “Aggressive”,– etc.
• do funds really inform potential contributors about riskiness?• we classified funds by 5 categories of riskiness based on
names• affinity between CVaRs distribution of 5 classes• affinity of random subsamples inside classes• two-sample Kolmogorov-Smirnov and Student’s t tests
Affinity of CVaR distributions
• Classes 1, 2, 3 are way more homogeneous than any other class or total sample
• Classes 1 and 3 are very close• Class 4 is similar to class 2 and class 3• Only class 5 is REALLY different:– Distinctive both by T and KS measures– Homogeneous (after many resamplings)
H1. Busted/plausible?
BUSTED!
H2. Are funds prone to herding?
• We have information about aggregated portfolio structure
• We can test for– Correlation – Granger causation
• in changes of portfolio shares• Between funds and between quartiles of
capitalization/efficiency
Granger causality: equities
Sum of causation in eq.chng by fund
Sum of causation by cap quartile
We tested the same for:
• Malmquist efficiency quartiles• All 4 submeasures– No result
• Matrix of granger causation for randomly generated matrices with same proportions, means, sd’s– Results are similar to real granger-causation
matrices
H2. Herding/!Herding?
PLAUSIBLE
What specification to use?
• DRS, VRS, IRS, CRS, FDH? • Input/output/two-way?• We want to have some predictable measure• to have good logit-regression, we need sample
with some funds efficient and some – not• too much “efficiency” => bad
Dea
Malmquist productivity
• Same questions about specification• For our results be comparable • we have to use the same set of specifications
for DEA and Malmquist productivity
Window dressing?
Wow!
Dropping expense+reward ratio
H2. Funds do not window-dress?
PLAUSIBLE