Time Series Data Mining with an Application to the Measurement of Underwriting Cycles

Underwriting cycles are believed to pose a risk management challenge to property-casualty insurers. The classical statistical methods that are used to model these cycles and to estimate their length assume linearity and give inconclusive results. Instead, we propose to use novel time series data Mining algorithms to detect and estimate periodicity on U.S. property-casualty insurance markets. These algorithms are in increasing use in data science and are applied to Big Data. We describe several such algorithms and focus on two periodicity detection schemes. Estimates of cycle periods on industry-wide loss ratios, for all lines combined and for four specific lines, are provided. One of the methods appears to be robust to trends and to outliers.     

Pension Fund Dynamics and Gains/Losses Due to Random Rates of Investment Return

A simple model for defined benefit pension plans with independent and identically distributed rates of investment return and a stationary membership is considered. Three methods of adjusting the normal cost as gains or losses arise are compared, and a suitable choice of amortization or spread period is made. We also investigate the evolution in time of the first and second moments of the pension fund and contribution levels.     

Optimal portfolio and spending rules for endowment funds

Review of Quantitative Finance and Accounting - Prior studies show that investor learning about earnings-based return predictors from academic research erodes return predictability. However, the...