scholarly journals The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 80
Author(s):  
Uditha Balasooriya ◽  
Johnny Siu-Hang Li ◽  
Jackie Li

We investigate the impact of model uncertainty on hedging longevity risk with index-based derivatives and assessing longevity basis risk, which arises from the mismatch between the hedging instruments and the portfolio being hedged. We apply the bivariate Lee–Carter model, the common factor model, and the M7-M5 model, with separate cohort effects between the two populations, and various time series processes and simulation methods, to build index-based longevity hedges and measure the hedge effectiveness. Based on our modeling and simulations on hypothetical scenarios, the estimated levels of hedge effectiveness are around 50% to 80% for a large pension plan, and the model selection, particularly in dealing with the computed time series, plays a very important role in the estimation. We also experiment with a modified bootstrapping approach to incorporate the uncertainty of model selection into the modeling of longevity basis risk. The hedging results under this approach may approximately be seen as a “weighted” average of those calculated from the different model candidates.

Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.


2017 ◽  
Vol 13 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Ned Kock

Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.


Intelligence ◽  
1981 ◽  
Vol 5 (2) ◽  
pp. 157-163 ◽  
Author(s):  
Lloyd G. Humphreys ◽  
Randolph K. Park

2020 ◽  
Vol 37 (2) ◽  
pp. 181-212
Author(s):  
Kenneth Wong ◽  
Jackie Li ◽  
Sixian Tang

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