https://hal.science/hal-03783062Abi Jaber, EduardoEduardoAbi JaberX - École polytechniqueVilleneuve, StéphaneStéphaneVilleneuveTSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’EnvironnementGaussian Agency problems with memory and Linear ContractsHAL CCSD2022Principal-AgentModelsContinuous-time control problems[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC][MATH.MATH-PR] Mathematics [math]/Probability [math.PR][QFIN.GN] Quantitative Finance [q-fin]/General Finance [q-fin.GN][SHS.ECO] Humanities and Social Sciences/Economics and FinanceAbi jaber, Eduardo2022-09-21 18:29:122023-06-06 17:18:132022-09-22 11:21:31enJournal articleshttps://hal.science/hal-03783062/documentapplication/pdf1Can a principal still offer optimal dynamic contracts that are linear in end-of-period outcomes when the agent controls a process that exhibits memory? We provide a positive answer by considering a general Gaussian setting where the output dynamics are not necessarily semi-martingales or Markov processes. We introduce a rich class of principal-agent models that encompasses dynamic agency models with memory. From the mathematical point of view, we develop a methodology to deal with the possible non-Markovianity and non-semimartingality of the control problem, which can no longer be directly solved by means of the usual Hamilton-Jacobi-Bellman equation. Our main contribution is to show that, for one-dimensional models, this setting always allows for optimal linear contracts in end-of-period observable outcomes with a deterministic optimal level of effort. In higher dimension, we show that linear contracts are still optimal when the effort cost function is radial and we quantify the gap between linear contracts and optimal contracts for more general quadratic costs of efforts.