Model Uncertainty and Financial Frictions: Implications for Optimal Monetary Policy

The last decades proved that policymaking without considering uncertainty is impracticable. In an environment of uncertainty, policymakers have doubts about the policy models they routinely use. This paper focuses specifically on the situation where uncertainty on the financial side of the economy leads to misspecification in the policy model. We describe a coherent strategy for policymakers who are averse to model misspecification and analyze optimal policy design in the face of Knightian uncertainty. To do so, we augment a financial dynamic stochastic general equilibrium model with model misspecification in a simple minimax framework where the central bank plays a zero-sum game versus a hypothetical evil agent. The policy is tailored to insure against the worst-case outcomes. We show that model ambiguity on the financial side requires a passive monetary policy stance. However, if the uncertainty originates from the supply side of the economy, an aggressive response of interest rate is required. We also show the impact of an additional macroprudential tool on the dynamics of the economy.

Network model of interpersonal links of Russian empire high bureaucracy. Problems and solutions

In the article some heuristic perspectives of usage of the methods of network analysis of interpersonal relations in the high bureaucracy of Russian empire network analysis are listed. The main problem of building such a model – ambiguity of the concept “link” – is stated. The ways of conceptual comprehension of this term are suggested to be acceptable for concrete-historical research. The interpersonal links detection algorithm, based on different quality sources with no need of microanalysis, is described.

Venture Capital Contracting with Ambiguity Sharing and Effort Complementarity Effect

In this paper, we established a continuous-time agency model in which an ambiguity-averse venture capitalist (VC) employs an ambiguity-neutral entrepreneur (EN) to manage an innovative project. We analyzed the connection between ambiguity sharing and incentives under double moral hazard. Applying a stochastic dynamic programming approach, we solved the VC’s maximization problem and obtained the Hamilton–Jacobi–Bellman (HJB) equation under a special form of the value function. We showed that the optimal pay-performance sensitivity was a fixed point of a nonlinear equation. The model ambiguity on the probability measure induced a tradeoff between ambiguity sharing and the incentive compensation that improved the EN’s pay-performance sensitivity level. Besides, we simulated the model and showed that when two efforts were complementary, the VC’s effort did not monotonically decrease with respect to the pay-performance sensitivity, while the EN’s effort did not monotonically increase in the pay-performance sensitivity level. More importantly, we found that as efforts tended to be more complementary, the optimal pay-performance sensitivity tended to approach those that maximized the efforts exerted by the EN and the VC.

Distributionally robust optimization with multiple time scales: valuation of a thermal power plant

The valuation of a real option is preferably done with the inclusion of uncertainties in the model, since the value depends on future costs and revenues, which are not perfectly known today. The usual value of the option is defined as the maximal expected (discounted) profit one may achieve under optimal management of the operation. However, also this approach has its limitations, since quite often the models for costs and revenues are subject to model error. Under a prudent valuation, the possible model error should be incorporated into the calculation. In this paper, we consider the valuation of a power plant under ambiguity of probability models for costs and revenues. The valuation is done by stochastic dynamic programming and on top of it, we use a dynamic ambiguity model for obtaining the prudent minimax valuation. For the valuation of the power plant under model ambiguity we introduce a distance based on the Wasserstein distance. Another highlight of this paper is the multiscale approach, since decision stages are defined on a weekly basis, while the random costs and revenues appear on a much finer scale. The idea of bridging stochastic processes is used to link the weekly decision scale with the finer simulation scale. The applicability of the introduced concepts is broad and not limited to the motivating valuation problem.

Liquidity Provision, Ambiguous Asset Returns and the Financial Crisis

For an economy with dysfunctional intertemporal financial markets the financial sector is modelled as a competitive banking sector offering deposit contracts. In a setting related to Allen and Gale (JoF, 1998) properties of the optimal liquidity provision are analyzed for illiquid assets with ambiguous returns.In the context of our model, ambiguity -- i.e. incalculable risk -- leads to dynamically inconsistent investor behaviour. If the financial sector fails to recognize the presence of ambiguity, unanticipated fundamental crises may occur, which are incorrectly blamed on investors 'loosing their nerves' and 'panicing'.The basic mechanism of the Financial Crisis resembles the liquidation of illiquid assets during a banking panic. The combination of providing additional liquidity and supporting distressed financial institutions implements the regulatory policy suggested by the model.A credible commitment to such 'bail-out policy' does not create a moral hazard problem. Rather, it implements the second best efficient outcome by discouraging excessive caution. Reducing ambiguity by increasing stability, transparency and predictability -- as suggested by ordo-liberalism and the 'Freiburger Schule' -- enhances ex-ante welfare.

The practical value of an explicit response model in quantitative uses Biacore™ biosensors

Suppose we aim to use data obtained by studying a biomolecular interaction system with a surface plasmon resonance (SPR) biosensor in quantifying some system feature. We assume a parametric mathematical model for biosensor response due to sources of mass such as analyte-ligand complexes. Some parameters represent interaction features, such as rate constants. Whenever we attempt to estimate parameters from data, we may obtain multiple estimates, regardless of the amount and quality of data. Inconveniently, we may be unable to distinguish between alternatives. This is problematic when alternative parameter values lead to very different predictions of system behaviour for a situation where we lack data. Anticipating this issue prior to data collection allows us to redesign the combination of planned experiments and model, replacing a certain failure to achieve our study’s aim with the possibility of success. The literature on SPR biosensors (and computational biology more generally) has paid little attention to this matter. In order to remedy this, it is appropriate to begin with a consideration of the assumed models. These are rarely specified completely, causing ambiguity that impedes scrutiny of their properties and comparison with other models. We demonstrate this by reviewing some model types seen in the Biacore™ biosensor literature. We propose to eliminate model ambiguity by providing a suitable framework for specifing models for biosensor data. This framework will aid future efforts to compose models for data arising from particular interaction mechanisms in a form that is amenable to scrutiny. We expect that the issues raised here will have relevance to the modelling of data obtained from other apparatus employed in quantifying binding behaviour.