Conference Agenda

Estimating the covariance matrix is a central problem in portfolio selection. The foundational shrinkage methodologies developed by Ledoit and Wolf (2004, 2017) suffer from two drawbacks: they are not designed to optimize out-of-sample portfolio performance and do not account for estimation errors in the means. In this paper, we propose a novel shrinkage covariance matrix estimator that addresses these two drawbacks. Specifically, we calibrate the shrinkage intensities in linear and nonlinear shrinkage estimators so that they maximize the expected out-of-sample portfolio performance. We find that this alternative calibration results in higher shrinkage intensities relative to the traditional approach and delivers a superior out-of-sample portfolio performance. Overall, our methodology is a one-step approach that estimates the covariance matrix and the optimal portfolio at the same time, which delivers large economic gains relative to the conventional two-step scheme.

People have difficulties to judge their own life expectancy, both relative to their peers and in absolute terms. Reasons for this are for example over-confidence or phenomena like the "grandfather clock". This leads to a subjective judgement of the (relative) attractiveness of different retirement products as subjective mortality beliefs differ from the life tables used by the insurance provider to set up the retirement contract. We show how to model and calibrate such beliefs and demonstrate their effect on retirement product demand. Among others, the results partially explain the annuity puzzle, indicating that the level of annuitization differs from the one proposed by economic models like the one of Yaari (1965).

We study the predictable representation property of the filtration obtained by progressively enlarging with a random time $\tau$ a reference filtration $\mathbb{F}$. We base our approach on decomposing $\tau$ into thin and thick parts. We prove a representation theorem along the entire time axis without assuming the avoidance condition for $\tau$ and discuss some examples of application to the context of Lévy processes. In particular, we obtain that if $\mathbb{F}$ is the natural filtration of a Brownian motion, the latter and the compensated occurrence process of $\tau$ constitute a basis of the representation when the immersion condition only is assumed.

The READI Actuaries Science Applied Research Program in Indonesia is looking for potential research related to risk estimation using climate information along with actuarial assumptions and methods. The monthly accumulated precipitation value is one of the climate information recorded by the Meteorology, Climatology, and Geophysical Agency of Indonesia (BMKG in Bahasa Indonesia). This weather factor can be used to calculate the financial risks of paddy crops in all provinces of Indonesia. The first stage of this calculation is forecasting the precipitation values in 2016-2017 from the data up to 2015 using Singular Spectrum Analysis (SSA) both Univariate and Multivariate accordingly, considering the three areas of calculations: Province, Region, and Country. The second step is to convert these rainfall values to calculate payouts based on several linear index insurance models, with 6 Million IDR per hectare per planting season as the maximum indemnity. Some additional analyses, such as comparisons between areas of calculations, between method combinations of the SSA itself, and between various missing data handling methods, are also included in this research. The current major findings of this research are the better performance of some SSA method combinations and the optimal linear index insurance model to reproduce the payouts. There are many factors to be considered when discussing good policies and the benefits of such insurance against agricultural risk.