A variety of mathematical and statistical methods have been developed and applied by researchers to solve problems of temporal disaggregation, the process of estimating unobserved sub-annual series from observed annual values. Despite a vast body of work evaluating the ability of different mathematical and statistical methods to accurately estimate the temporal dynamics of target series, few empirical papers have attempted to establish the conditions under which some of these methods may have an advantage over competing models. While most empirical studies have focused on applying these methods to relatively well-behaved series, this paper examines to what extent the volatility of the target series being estimated may be a factor in the relative performance of four different mathematical methods of temporal disaggregation: the Denton proportional first difference method (with and without a related indicator series), the Causey-Trager growth rate preservation method, and the cubic spline interpolation method. Using source data provided by the National Association of Insurance Commissioners (NAIC) for twenty-three lines of property and casualty insurance between 2002 and 2012, we employ each of the four methods to estimate quarterly output by line of insurance for BEA's Industry Economic Accounts. To our knowledge, this is the first empirical study of its kind to use insurance industry data to examine the performance of these methods as it relates specifically to the volatility of the target series.