Zoom Conference ID:813-2065-1303; Code:679275
Kenneth Q. Zhou (Assisstant Professor），Arizona State University
Long-range dependence (LRD) in mortality dynamics has been identified and studied in the recent actuarial literature. The non-Markovian feature caused by LRD can raise new research questions and challenges in actuarial pricing and risk management. The first part of this talk discusses a new continuous-time modeling approach that uses a combination of independent Brownian motion and fractional Brownian motion to model LRD in mortality dynamics. To obtain mortality sensitivity measures in the presence of LRD, a novel derivation method using directional derivatives is considered. We also provide empirical illustrations to compare the performance of different sensitivity measures in natural hedging of longevity risk. In discrete-time modeling, short memory ARIMA processes are typically used to model the evolution of mortality differentials between different populations. In the second part of this talk, we examine how mortality differentials can be modelled by long memory ARFIMA processes. We also study how index-based longevity hedges should be calibrated if mortality differentials follow long memory processes.
Brief introduction of the speaker:
Kenneth Q. Zhou is an Assistant Professor of Actuarial Science at Arizona State University. He received his PhD in actuarial science from the University of Waterloo in 2019. His research interests include stochastic mortality modeling, longevity risk management, insurance data-analytics and securitization. He has published in top-tier actuarial and insurance journals, including IME and JRI. He is a Fellow of the Society of Actuaries and an Associate of the Canadian Institute of Actuaries, and was a Society of Actuaries James C. Hickman scholar from 2015 to 2019.