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Asset Pricing of Financial Institutions: The Cross-Section of Expected Insurance Stock Returns (joint work with M. Eling & A. Milidonis) Early stage paper.

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Presentation on theme: "Asset Pricing of Financial Institutions: The Cross-Section of Expected Insurance Stock Returns (joint work with M. Eling & A. Milidonis) Early stage paper."— Presentation transcript:

1 Asset Pricing of Financial Institutions: The Cross-Section of Expected Insurance Stock Returns (joint work with M. Eling & A. Milidonis) Early stage paper Semir Ben Ammar University of St. Gallen, Switzerland APRIA conference Moscow, July 29th 2014

2 Agenda 1. Motivation 2. Theory 3. Literature 4. Data 5. Empirical results 6. To Do 7. Conclusion

3 Motivation Financial Institutions are generally excluded from cross-sectional asset pricing tests. Asset pricing models should price any asset Exception: Viale, Kolari, and Fraser (JBF, 2009) look at bank stocks: What about insurance stocks? Insurance stocks are somewhat special in terms of their risk exposure (i.e. catastrophes, casualty, longevity, etc.)  Key question: Are (well-) known asset pricing models able to price insurance stocks?

4 Theory Asset pricing models should identify a linear relationship between expected return and beta. Usually portfolios are used to conduct asset pricing test (sorted by a characteristic)  Monotonic pattern?  Is this pattern captured by the asset pricing model? Here: Number of insurance stocks too small to create a sufficient number of portfolios  We use individual stocks

5 Theory Individual stocks create Errors-in-variables (EIV) problem  New instrumental variables methodology How can we test an asset pricing model with individual stocks? Run Fama-MacBeth regressions: 1. Run time-series regression 𝑅 𝑖,𝑡 − 𝑅 𝑓,𝑡 = 𝛼 𝑖 + 𝑘=1 𝐾 𝛽 𝑖,𝑘 𝑓 𝑘,𝑡 + 𝜀 𝑖,𝑡 2. Then use the beta coefficients 𝑅 𝑖,𝑡 − 𝑅 𝑓,𝑡 = 𝑧 𝑡 + 𝑘=1 𝐾 𝜆 𝑘,𝑡 𝛽 𝑘,𝑖,𝑡 + 𝛼 𝑖.𝑡

6 Theory Hypotheses: 𝐻 0 𝑧=0 : 𝑧=0 𝐻 0 𝜆=0 : 𝜆=0 𝐻 0 𝜆=𝜇 : 𝜆−𝜇=0
We test 10 different asset pricing models including CAPM, Fama/French-3, Fama/French-5, Carhart, Pastor/Stambaugh, Petkova, Cummins/Lamm-Tennant, Rubinstein/Leland, Hahn/Lee, Adrian/Etula/Muir.

7 Literature Harrington (1983, JRI) investigates life insurers, finding “some evidence of a significant relationship between mean returns and systematic risk [i.e. the CAPM-beta], but […] also […] a significant relationship between mean returns and measures of nonsystematic risk […].” Cummins and Harrington (1988, JRI) addressing property-casualty insurers finds that the CAPM is correctly specified during the period 1980 – 1983, but inconsistent in earlier periods. Since then, only Bajtelsmit, Villupuram, and Wang (WP, 2014) looked at the cross-section of life insurers. Cummins and Phillips (2005) investigate cost of equity for P&C insurers using CAPM and FF-3 model. They find that the costs of capital estimates of FF-3 model are significantly higher than those of the CAPM  Not asset pricing in a narrow sense

8 Data Datassource: CRSP, Compustat, university websites publishing their factors. 217 U.S. insurance stocks in the time period January 1987 to January 2012: Summary statistics: Year Life/Health Non-Life Total 2002 27 71 98 2003 29 72 101 2004 25 75 100 2005 24 79 103 …. Life/Health Non-Life Mean (monthly in %) 1.17 1.08 Std. dev. 10.71 10.41 Min -78.37 -83.48 Max 143.75 244.98 Skew 0.65 2.08 Kurtosis 14.20 36.13 # of observations 8,381 23,327

9 Empirical results (1/4): Life/Health
CAPM: 𝝀 𝟎 𝝀 𝑴 Adj.R2 CAPM Estimate 0.42 0.40* 3.4% FM t-stat (1.60) (1.79) APT-motivated: 𝝀 𝟎 𝝀 𝑴 𝝀 𝑺𝑴𝑩 𝝀 𝑯𝑴𝑳 𝝀 𝑴𝑶𝑴 𝝀 𝑳𝑰𝑸 𝝀 𝑻𝑬𝑹𝑴 𝝀 ∆𝑻𝑬𝑹𝑴 𝝀 𝑫𝑬𝑭 𝝀 ∆𝑫𝑬𝑭 𝝀 𝑺𝒌𝒆𝒘 𝝀 𝑲𝒖𝒓𝒕 Adj.R2 Fama-French 3-factors (1993) Estimate 0.40 0.38 -0.20 0.02 9.7% FM t-stat (1.38) (1.14) (-0.81) (0.06) Carhart 4-factors (1997) 0.37 0.46 -0.01 0.12 10.2% (1.10) (1.20) (-0.78) (-0.04) (0.29) Pastor-Stambaugh Liquidity (2003) 0.44 0.31 0.26 0.33 11.4% (1.48) (0.95) (-1.02) (0.05) (-0.83) Hahn and Lee (2006) 0.72 -0.02 -0.46 0.05** 12.0% (1.11) (1.61) (-0.09) (-1.19) (2.16) (-1.06) Fama-French 5-factors (1993) 0.51 -0.14 -0.09 0.11 0.10 10.8% (1.70) (0.88) (-0.51) (-0.28) (0.75) RL 3-Moment CAPM 0.62* 0.19 -0.25 -0.10 7.5% (1.82) (0.46) (-0.40)

10 Empirical results (2/4): Life/Health
ICAPM-motivated: 𝝀 𝟎 𝝀 𝑴 𝝀 𝒖 𝑻𝑬𝑹𝑴 𝝀 𝒖 𝑫𝑬𝑭 𝝀 𝒖 𝑫𝑰𝑽 𝝀 𝒖 𝑹𝑭 𝝀 𝑩𝑫𝑳𝒆𝒗 𝝀 𝑰𝒏𝒔𝑳𝒆𝒗 𝝀 𝑭𝒊𝒏𝑳𝒆𝒗 Adj.R2 Petkova (2006) Estimate 0.39 0.35 1.44 0.76 1.07 0.86* 9.6% FM t-stat (1.38) (1.26) (0.72) (-0.45) (1.33) (1.70) Cummins and Lamm-Tennant (1994) 0.37 0.50 0.22 0.06 7.6% (1.22) (1.24) (0.94) (0.34) Adrian, Etula, and Muir (2013) 1.54 2.81 2.7% (1.66) (1.43)

11 Empirical results (3/4): Non-Life
CAPM: 𝝀 𝟎 𝝀 𝑴 Adj.R2 CAPM Estimate 0.51*** 0.13 2.1% FM t-stat (2.97) (0.59) APT-motivated: 𝝀 𝟎 𝝀 𝑴 𝝀 𝑺𝑴𝑩 𝝀 𝑯𝑴𝑳 𝝀 𝑴𝑶𝑴 𝝀 𝑳𝑰𝑸 𝝀 𝑻𝑬𝑹𝑴 𝝀 ∆𝑻𝑬𝑹𝑴 𝝀 𝑫𝑬𝑭 𝝀 ∆𝑫𝑬𝑭 𝝀 𝑺𝒌𝒆𝒘 𝝀 𝑲𝒖𝒓𝒕 Adj.R2 Fama-French 3-factors (1993) Estimate 0.57*** 0.24 -0.13 -0.23 5.5% FM t-stat (3.27) (1.00) (-0.67) (-1.17) Carhart 4-factors (1997) 0.55*** 0.33 -0.11 -0.29 0.45* 6.5% (3.15) (1.31) (-0.59) (1.51) (1.84) Pastor-Stambaugh Liquidity (2003) 0.60*** 0.17 -0.10 -0.27 6.7% (3.35) (0.72) (-0.53) (-1.18) (-0.93) Hahn and Lee (2006) 0.51*** 0.31 -0.18 -0.24 0.01 7.5% (3.01) (1.26) (-0.97) (-1.21) (1.02) (0.81) Fama-French 5-factors (1993) 0.23 0.19 0.20 0.08 0.06 7.7% (3.47) (0.95) (-0.51) (-1.47) (1.55) RL 3-Moment CAPM 0.48*** 0.27 -0.17 -0.34** 5.8% (2.65) (-2.34)

12 Empirical results (4/4): Non-Life
ICAPM-motivated: 𝝀 𝟎 𝝀 𝑴 𝝀 𝒖 𝑻𝑬𝑹𝑴 𝝀 𝒖 𝑫𝑬𝑭 𝝀 𝒖 𝑫𝑰𝑽 𝝀 𝒖 𝑹𝑭 𝝀 𝑩𝑫𝑳𝒆𝒗 𝝀 𝑰𝒏𝒔𝑳𝒆𝒗 𝝀 𝑭𝒊𝒏𝑳𝒆𝒗 Adj.R2 Petkova (2006) Estimate 0.54*** 0.06 -0.30 1.56 -0.07 0.50* 6.9% FM t-stat (3.07) (0.27) (-0.23) (1.53) (-0.16) (1.87) Cummins and Lamm-Tennant (1994) 0.26 -0.29** -0.09 5.2% (3.19) (1.05) (-2.13) (-0.65) Adrian, Etula, and Muir (2013) 1.56** 0.31 2.2% (2.01) (0.24)

13 To Do Use of portfolios Restrict ourselves to Life and P&C insurance stocks (to avoid the mix of different effects Compare models using Hansen-Jagannathan distance Further robustness checks using different winsorization Time-varying betas where the betas are estimated over a 36 or a 48 rolling window period Combine model results

14 Preliminary conclusion
Life insurance stocks: Changes in the slope of the yield curve and innovations in the 1-Month T-Bill yield are priced. Non-life insurance stocks: Momentum, kurtosis, insurance leverage, and innovations in the 1-Month T-Bill yield are priced. Most common asset pricing model (i.e. CAPM, FF-3 model) has no explanatory power)


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