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Optimal Portfolio A llocation using TLI . Tommaso Gabrieli University of Reading Davide Manstretta IPD. Introduction. Motivation Property market returns (IPD) are based on Valuers ’ appraisal Vast literature argues that data underestimates true volatility (smoothing)
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Optimal Portfolio Allocation using TLI TommasoGabrieliUniversity of Reading DavideManstrettaIPD
Introduction • Motivation • Property market returns (IPD) are based on Valuers’ appraisal • Vast literature argues that data underestimates true volatility (smoothing) • New IPD series Transaction-Linked Index (TLI) should represent true returns and correct for the problem • Devaney and Martinez Diaz JPR 2011 • Research Questions: • Is TLI series different form de-smoothed Valuers’ Based Index (VBI) series? • Implications for portfolio allocation?
Agenda • Introduction and Results Overview • A little bit of theory: • Problem definition • Empirical Results: • Differences between TLI and de-smoothed VBI • Portfolio Allocation analysis • Conclusions • Main Result • TLI and de-smoothed VBI are very different; strong implications for portfolio allocation
Smoothing • Assumptions: • Property Market Returns are Valuation Based • May Lag Market Movements – Distorts Correlation • May Be “Smoothed” – Understates the Volatility • “De-smoothing” Procedures • Remove the Impact of Valuations in Data • Reported return is a blend of “true” and previous return • De-smoothing: • Rvt= a Rvt-1 + (1-a)Rt Therefore Rt = {Rvt - a Rvt-1 } / (1-a) where a is the “smoothing parameter” • Vast literature: • Blundell and Ward (1987), Quan and Quigley (1991), Brown and Matysiak (2000), Geltner et al. (2002) and many others…
Conclusion • Findings • According to TLI, property is a very attractive asset class • TLI and De-smoothed VBI are very different, which is theoretically worrying... • Extensions • Annual Data • Implied smoothing parameter
Thank you for your attention Questions? Comments?