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Some stylized facts of Russian private pension funds. Didenko Alexander International Financial Laboratory Alexander.didenko@gmail.com. Questions. W hat funds are efficient? What metrics to use? Is there any persistence? Do they inform customers about risks?

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some stylized facts of russian private pension funds

Some stylized facts of Russian private pension funds

Didenko Alexander

International Financial Laboratory

Alexander.didenko@gmail.com

questions
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
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
DEA - conceptual model

Input 1

Output 1

Production Plans

Input 2

Output 1

Input N

Output 1

data envelopment analysis
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
malmquist index
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
DEA – general model for funds

Financial Capital

Return

Pension Funds

Risk

Market Share

Human Capital

dea our specificaion
DEA – our specificaion

Active return

CVaR

Pension Funds

NAV Share

E+R Ratio

Diversification

slide10
CVaR
  • Wuertz, Chalabi, Chen, Ellis (2009);
  • RUPAI, RUPCI, RGBI
  • Alpha=0.05
  • Weekly data
  • Average quarterly CVaR
diversification
Diversification
  • There are plenty of D. measures
  • We use that of Goetzmann, Kumar, 2008
h1 funds convey useful info in names
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
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)
h2 are funds prone to herding
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
we tested the same for
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
what specification to use
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
malmquist productivity
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