Continuous vs. discrete time: Some computational insights
研究发现，求解不完全市场模型时连续时间方法比离散时间快很多，主要原因是隐式有限差分法（连续时间常用工具）的高效性，该方法可视为Howard改进算法的特例，通过稀疏矩阵操作实现。在离散时间中引入类似方法可消除运行时间差异。
Solving a workhorse incomplete markets model in continuous time is many times faster compared to its discrete time counterpart. This paper dissects the computational discrepancies and identifies the key bottlenecks. The implicit finite difference method – a commonly used tool in continuous time – accounts for a large share of the difference. This method is shown to be a special case of Howard’s improvement algorithm, efficiently implemented by relying on sparse matrix operations. By representing the policy function with a transition matrix it is possible to formulate a similar procedure in discrete time, which effectively eliminates the differences in run-times entirely.