Bid and Ask spreads for the cap and floor contracts under the Liouville fractional Vasicek model

This paper presents bid and ask formulas for cap and floor contracts prices byusing Wang transform under a Liouville fractional Vasicek (LfVasicek) interest rate model. To do this, the parameters of the model are calibrated by using the Newton-Raphson (NR) method. Then the standard and Liouville fractional versions of the Vasicek model are compared by the Bayes information criterion (BIC). Finally, we obtain the bid-ask boundaries for interest rate amount and cap and foor prices.

Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

Bid and Ask spreads for the cap and floor contracts under the Liouville fractional Vasicek model

This paper presents bid and ask formulas for cap and floor contracts prices byusing Wang transform under a Liouville fractional Vasicek (LfVasicek) interest rate model. To do this, the parameters of the model are calibrated by using the Newton-Raphson (NR) method. Then the standard and Liouville fractional versions of the Vasicek model are compared by the Bayes information criterion (BIC). Finally, we obtain the bid-ask boundaries for interest rate amount and cap and foor prices.

Fitting multi-population mortality models to socio-economic groups

We compare results for 12 multi-population mortality models fitted to 10 distinct socio-economic groups in England, subdivided using the Index of Multiple Deprivation. Using the Bayes Information Criterion to compare models, we find that a special case of the common age effect (CAE) model fits best in a variety of situations, achieving the best balance between goodness of fit and parsimony. We provide a detailed discussion of key models to highlight which features are important. Group-specific period effects are found to be more important than group-specific age effects, and non-parametric age effects deliver significantly better results than parametric (e.g. linear) age effects. We also find that the addition of cohort effects is beneficial in some cases but not all. The preferred CAE model has the additional benefit of being coherent in the sense of Hyndman et al. ((2013) Demography50(1), 261–283); some of the other models considered are not.

Kernel integration by Graphical LASSO

Integration of unstructured and very diverse data is often required for a deeper understanding of complex biological systems. In order to uncover communalities between heterogeneous data, the data is often harmonized by constructing a kernel and numerical integration is performed. In this study we propose a method for data integration in the framework of an undirected graphical model, where the nodes represent individual data sources of varying nature in terms of complexity and underlying distribution, and where the edges represent the partial correlation between two blocks of data. We propose a modified GLASSO for estimation of the graph, with a combination of cross-validation and extended Bayes Information Criterion for sparsity tuning. Furthermore, hierarchical clustering on the weighted consensus kernels from a fixed network is used to partitioning the samples into different classes. Simulations show increasing ability to uncover true edges with increasing sample size and signal to noise. Likewise, identification of non existing edges towards disconnected nodes is feasible. The framework is demonstrated for integration of longitudinal symptom burden data from the 2nd and 3rd year of life with 21 diseases precursors as well as the development of asthma and eczema at the age of 6 years from 403 children from the COPSAC2010 mother-child cohort, suggesting that maternal predisposition as well as being born preterm indirectly lead to higher risk of asthma via increased respiratory symptom burden.

Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes

The Penman–Monteith equation (FAO-56) is accepted as the standard model for estimating reference evapotranspiration (ETo). However, the major obstacle to using FAO-56 widely is that it requires numerous climatic data. The Hargreaves–Samani (HS) method is frequently used for the calculation of ETo since it is based on measurements of daily minimum and maximum air temperature alone. Those are commonly recorded at many meteorological stations throughout the world. It is the objective of this paper to evaluate the quality of HS and calibrate the coefficients of this method for different climates as represented by the Köppen classification. Estimated values are compared with Penman–Monteith ETo values in terms of the coefficient of efficiency Ceff as well as the root mean square error, the mean absolute error and the Bayes information criterion. The Penman–Monteith equation for ETo (FAO-56) is based on physics and known to provide best estimates of ETo. The results of our work show that the correlation between long-term monthly means of HS and FAO-56 can be improved significantly by introducing climate-class specific coefficients.