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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. 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

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  1. Some stylized facts of Russian private pension funds Didenko Alexander International Financial Laboratory Alexander.didenko@gmail.com

  2. Questions • What funds are efficient? • What metrics to use? • Is there any persistence? • Do they inform customers about risks? • Do they have behavioral biases?

  3. 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

  4. DEA - conceptual model Input 1 Output 1 Production Plans Input 2 Output 1 Input N Output 1

  5. 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

  6. Example from Coopers et al.

  7. 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

  8. DEA – general model for funds Financial Capital Return Pension Funds Risk Market Share Human Capital

  9. DEA – our specificaion Active return CVaR Pension Funds NAV Share E+R Ratio Diversification

  10. CVaR • Wuertz, Chalabi, Chen, Ellis (2009); • RUPAI, RUPCI, RGBI • Alpha=0.05 • Weekly data • Average quarterly CVaR

  11. Diversification • There are plenty of D. measures • We use that of Goetzmann, Kumar, 2008

  12. 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

  13. 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)

  14. H1. Busted/plausible? BUSTED!

  15. 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

  16. Granger causality: equities

  17. Sum of causation in eq.chng by fund

  18. Sum of causation by cap quartile

  19. 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

  20. H2. Herding/!Herding? PLAUSIBLE

  21. 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

  22. Dea

  23. 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

  24. Window dressing? Wow!

  25. Dropping expense+reward ratio

  26. H2. Funds do not window-dress? PLAUSIBLE

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