... framework. As it is a powerful approach, it can also become dangerous when used by unskilled hands, or when misused intentionally. This is exactly the reason why we made our concept of Bayesian Due Diligence the central pillar of BRM.

Basically, we agree that the concept of heavy-tailed distributions is a powerful one, but we see a fundamental challenge when it comes to Black Swans. Please mind that our concept of Black Swans differs from that of Taleb. For us, a Black Swan is an event that people cannot imagine, or refrain from imagining. This relates to cognitive and psychological constraints, respectively. If, however, an analyst cannot image a specific event, for whatever reason, how can he address it by using heavy-tailed distributions? We do not suggest that we have an answer for this question. We just want to raise awareness for this issue.

... in general. We neither aimed at discussing how financial risk management dealt with systemic risk before the contemporary financial crisis hit, and how the financial risk community reacted to the outbreak of the crisis. Because we wanted to be short and crisp, we refrained from giving details how we developed our concept. As we thought that giving one or two examples of applying our concept would compromise its generality, we haven chosen the format as it stands.

Our paper is not a standard economics paper. A standard economics paper follows the status quo plus epsilon approach: it builds on an established concept, and adds a little to it. Typically, it uses a well-established formal apparatus. Obviously, it is at the discretion of the editors whether they want to confine their journal to publishing such papers. The discipline of economics badly needs platforms for sharing innovative concepts. We think that one of the reasons of the financial crisis is that economists stuck too long to inappropriate concepts.

As subjectivist Bayesians we argue both on the descriptive and the normative level. We think that humans often actually behave like Bayesians. And we think that they have good reasons for this behavior. To what extent our empirical hypothesis is valid, is an important research question. We would, however, remark that in empirical research one should discriminate between whether someone claims to be a frequentist, and whether he actually is. It is our understanding that formally, the Basel II regulations are neutral concerning frequentist or Bayesian approaches, but actually gave rise to the dominance of frequentist techniques in bank risk management. We would not be surprised to find that a considerable share of the actors who applied these frequentist tools were well aware of their shortcomings but had substantial incentives to ignore these shortcomings. At least we encountered quite some managers who pretended to believe in the efficient market hypothesis but actually thought differently.

We did not want to write a paper on the Basel regulations. We could, however, exemplify the need of our BRM approach in general, and of our Bayesian Due Dilligence in particular, when discussing possible future Basel regulations. Running practically all banking risk management on frequentist tools that look five years back at maximum is not a really convincing approach given the history of financial markets. It is a stylized fact of financial markets that at least once a century, speculative bubbles burst. This was well known even before Reinhart and Rogoff published their monograph. BRM offers a concept for including expert knowledge into risk management, and the knowledge of economic historians is but one example. This, however, creates the risk that expert input is used to provide arguments for even higher leverage. A reasonable regulation must keep this risk in check. Bayesian Due Diligence is the conceptual frame for this as it asks the risk managers to document and be able to defend their necessarily subjective choices.

... prior.

@2: We would love to discuss the issue of BRM and plurality, but this would leave us deeply into philosophical ground. If the editors encourage us to do so, we are fine.

@3: We would exemplify it by contrasting the inability of established financial risk management models to mobilize the expertise of economic historians with BRM, which gives room for mobilizing it. Cf. the last paragraph of our answer to referee report #1.

@4: Whenever there is sufficient data, we are fine with frequentist methods. Typically, frequentist methods ask for many, but not too many data, which is exactly their problem when in many real life situations only few data are available. The big danger of BRM is that its potential can be misused by intentionally selecting biased expertise. For addressing this danger, we made our Bayesian Due Diligence the central pillar of our concept.

@5&@6: This is a misunderstanding. We do not present a specific model but a general concept. In our own work, we used a rather diverse set of models and methods, which have only in common that they were subjective Bayesian approaches. We could talk about these models, but this would no longer be a short and crisp conceptual paper. Cf. paragraph 1 of our answer to referee report #1.