8 factors in the BKT model given by the seven FH risk factors and the return of

8 factors in the bkt model given by the seven fh risk

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=8 factors in the BKT model, given by the seven FH risk factors and the return of our correlation swap (Model 2). Using GLS estimators, we estimate a negative market price of correlation risk with respect to models 1 and 2, both with and without Shanken’s asymptotic correction. The point estimate for the correlation factor risk premium is highly statistically significant, with t -statistics of 4.14 and 4.06, respectively, in each model. 19 In contrast, the GLS point estimates for other well-known 18 The EIV problem has a number of potential consequences in two-step least squares procedures. First, if standard errors do not include information that beta coefficients are measured with error, the implied t -statistics might overstate the precision of the risk premium estimates. Second, different estimators might have substantially different properties when the linear factor model is misspecified, either because of a missing factor or because of a latent nonlinearity. Finally, least squares estimators of risk premia in the second step might be biased in finite samples. 19 The t -statistics after Shanken’s correction are 4.06 and 3.92, respectively. 609 Downloaded from by Queen Mary University of London user on 02 March 2018
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The Review of Financial Studies / v 27 n 2 2014 Table 5 The cross-section of hedge fund excess returns and correlation risk exposures Panel A: Model 1 (Correlation risk and market risk) With Shanken’s correction GLS WLS GLS WLS Intercept 0 . 10 0 . 16 0 . 10 0 . 16 t -stat (3 . 74) (2 . 31) (3 . 47) (2 . 17) Correl risk 4 . 33 4 . 54 4 . 33 4 . 54 t -stat (4 . 14) (2 . 7) (4 . 06) (2 . 58) Mkt risk 0 . 02 0 . 57 0 . 02 0 . 57 t -stat (0 . 07) (1 . 41) (0 . 07) (1 . 38) Panel B: Model 2 (Correlation risk factor and FH(2004) model) Intercept 0 . 10 0 . 18 0 . 10 0 . 18 t -stat (3 . 74) (3 . 4) (3 . 32) (3 . 09) Correl risk 4 . 27 2 . 61 4 . 27 2 . 61 t -stat (4 . 06) (1 . 9) (3 . 92) (1 . 79) Mkt risk 0 . 01 0 . 39 0 . 01 0 . 39 t -stat (0 . 04) (1 . 06) (0 . 04) (1 . 04) SCMBC 0 . 58 0 . 47 0 . 58 0 . 47 t -stat (2 . 09) (1 . 33) (2 . 05) (1 . 26) BD10RET 0 . 04 0 . 36 0 . 04 0 . 36 t -stat (0 . 19) (1 . 33) (0 . 19) (1 . 24) BAAmTSY 0 . 03 0 . 08 0 . 03 0 . 08 t -stat (0 . 15) (0 . 36) (0 . 14) (0 . 34) PTFSBD 3 . 18 2 . 06 3 . 18 2 . 06 t -stat (2 . 53) (1 . 19) (2 . 44) (1 . 12) PTFSFX 2 . 89 4 . 79 2 . 89 4 . 79 t -stat (1 . 82) (2 . 11) (1 . 74) (1 . 98) PTFSCOM 0 . 37 2 . 12 0 . 37 2 . 12 t -stat (0 . 32) (1 . 26) (0 . 31) (1 . 18) This table reports estimates for the risk premia on the market index, the Fung and Hsieh (2004) factors and the correlation risk factor (CR). Portfolios are formed based on rolling beta estimates. In Panel A, we report results for the market and the correlation risk factor (Model 1). In Panel B, we report results for the BKT eight- factor model (Model 2). The estimation methods are GLS and WLS versions of the (Fama-MacBeth) two-pass regression methodology. t -statistics are in brackets. t -statistics in Columns 4 to 5 are calculated using standard errors based on Shanken (1992) errors-in-variables (EIV) adjustment. The cross-sectional regressions are based on 75 portfolios (based on a sort using the market betas). Each year from January 1999 to 2011 funds are sorted into these portfolios based on their betas calculated using the previous 36 monthly returns. The sample period is
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