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Australian investment performance 1959 to 2017 (and investment assumptions for stochastic models)

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Added , Speaker Colin Grenfell (SuperEasy Pty Ltd), Thomas Sneddon (Willis Towers Watson), VICA2018, in AFIR / ERM IACA
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Speaker(s): Colin Grenfell (SuperEasy Pty Ltd), Thomas Sneddon (Willis Towers Watson)

Further information is presented in a detailed paper: Grenfell_Sneddon_Paper.pdf

This paper analyses 58 years, or 232 quarters, of Australian investment performance from 30 June 1959 (and earlier for some sectors) to 30 June 2017. The paper updates previous papers presented in 2005, 2007, 2009 and 2013 to the Institute of Actuaries of Australia and to ICA 2010 and 2014. The aim is to assess whether the methodology for determining assumptions in the previous papers is still robust enough to produce reasonable financial assumptions now that a further four years of financial data, covering a continuing tumultuous period in global markets, is being examined.

The analysis covers eleven investment classes and four key financial indicators. For each of these 15 "sectors" the annualised average results are tabulated and summarised for:

  • risk margins (over 10-year bond rates)
  • coefficients of variation
  • skewness
  • kurtosis
  • cross-correlations
  • auto-correlations.

From these results, assumptions are developed for the mean, standard deviation, skewness, kurtosis, cross-correlations and auto-correlations for each sector. The assumptions are intended for both medium-term (3 to 10-year) and long-term (10 to 60-year) modelling. These assumptions are primarily designed for use, until about 2020, in stochastic investment and asset/liability modelling. After about two or three years they should be updated.

The paper also analyses:

  • economic cycles using a sine curves technique
  • the continuing impact of the Global Financial Crisis, comparing it with previous market downturns in 1974, 1987 and 2002/03
  • auto-correlations for both Australian share returns and 10-year bond rates over two non-overlapping half-data periods.

Tags: Models

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