Risk-sensitive Investment Research Articles

Risk-Sensitive Investment Management: A Guide for Quants

Risk-sensitive investment management (RSIM) has emerged as a powerful approach that leverages the mathematics of stochastic control to address dynamic investment management problems. This note discusses RSIM and examines three practical implications of RSIM for investment managers. Firstly, RSIM models serve as a valuable tool for understanding and explaining the outcomes of realistic dynamic models. For example, they offer closed-form approximations of stochastic programming models. Secondly, RSIM sheds light on crucial investment management questions. RSIM enables practitioners to analyze the impact of various factors on investment performance, unravel the dynamics between active and passive management strategies, investigate the mechanics of learning within investment decisions, assess the value of expert opinions, and evaluate the effectiveness of stress test scenarios. Lastly, RSIM casts light on perilous strategies and practices that can lead to catastrophic losses for investment funds, such asexcessive betting or overbetting.

On the Separation of Estimation and Control in Risk-Sensitive Investment Problems under Incomplete Observation

On the Separation of Estimation and Control in Risk-Sensitive Investment Problems under Incomplete Observation

Risk-sensitive investment in a finite-factor model

A new jump diffusion regime-switching model is introduced, which allows for linking jumps in asset prices with regime changes. We prove the existence and uniqueness of the solution to the risk-sensitive asset management criterion maximization problem in this setting. We provide an ODE for the optimal value function, which may be efficiently solved numerically. Relevant probability measure changes are discussed in the appendix. The recently introduced approach of Klebaner and Liptser (2013) is used to prove the martingale property of the relevant density processes.

If Numbers Can Speak, Who Listens? Creating Engagement and Learning for Effective Uptake of DRR Investment in Developing Countries.

Introduction: With a renewed emphasis on evidence-based risk sensitive investment promoted under the Sendai Framework for Disaster Risk Reduction 2015-2030, technical demands for analytical tools such as probabilistic cost-benefit analysis (CBA) will likely increase in the foreseeable future. This begs a number of pragmatic questions such as whether or not sophisticated quantitative appraisal tools are effective in raising policy awareness and what alternatives are available.Method: This article briefly reviews current practices of analytical tools such as probabilistic cost-benefit analysis and identifies issues associated with its applications in small scale community based DRR interventions.Results: The article illustrate that while best scientific knowledge should inform policy and practice in principle, it should not create an unrealistic expectation that the state-of-the art methods must be used in all cases, especially for small scale DRR interventions in developing countries, where data and resource limitations and uncertainty are high, and complex interaction and feedback may exist between DRR investment, community response and longer-term development outcome. Discussion: Alternative and more participatory approaches for DRR appraisals are suggested which includes participatory serious games that are increasingly being used to raise awareness and identify pragmatic strategies for change that are needed to bring about successful uptake of DRR investment and implementation of DRR mainstreaming.

Risk-Sensitive Investment in a Market with Animal Spirits

A new jump diffusion regime-switching model is introduced, which allows for linking jumps in asset prices with regime changes. We prove the existence and uniqueness of the solution to the risk-sensitive asset management criterion maximisation problem in this setting. We provide an ODE for the optimal value function, which may be efficiently solved numerically. Relevant probability measure changes are discussed in the appendix. The approach of Klebaner & Lipster (2014) is used to prove the martingale property of the relevant density processes.