Minimax Analysis of Monetary Policy Under Model Uncertainty

Recently there have been several studies that examined monetary policy under model uncertainty. These studies formulated uncertainty in a number of different ways. One of the prominent ways to formulate model uncertainty is to form a non-parametric set of perturbations around some nominal model where the set is structured so that the uncertainty is focused on potentially important weaknesses of the model. Unfortunately, previous efforts were unable to compute exact optimal policy rules under this general formulation of uncertainty. Moreover, for those special cases when the robust rules were computed, the degree of their aggressiveness was often counterintuitive in light of conventional Brainard/Bayesian wisdom that policy under uncertainty should be conservative. This paper,therefore, consists of three different exercises concerning minimax analysis of policy rules under model uncertainty. First, the minimax approach is compared with the Bayesian one in a stylized Brainard (1967) setting. Strong similarities between recommendations of the two approaches are found. Next, a more realistic setting such as in Onatski and Stock (1999) is considered. A characterization of the worst possible models corresponding to the max part of the minimax scheme is given. It is shown that the worst possible models for very aggressive rules, such as the H-infinity rule, have realistic economic structure whereas those for passive rules, such as the actual Fed's policy, are not plausible. Thus, the results of minimax analysis presented in Onatski and Stock (1999) might be biased against the passive rules. Finally, exact optimal minimax policy rules for the case of slowly time-varying uncertainty in the case of the Rudebusch and Svensson's (1998) model are computed. The optimal rule under certainty turns out to be robust to moderate deviations from Rudebusch and Svensson's model.

Privileged information exacerbates market volatility

We study how asymmetric information affects market volatility in a linear setup where the outcome is determined by forecasts about this same outcome. The unique rational expectations equilibrium will be stable when it is the only rationalizable solution. It has been established in the literature that stability is obtained when the sensitivity of the outcome to agents' forecasts is less than 1, provided that this sensitivity is common knowledge. Relaxing this common knowledge assumption, instability is obtained when the proportion of agents who a priori know the sensitivity is large, and the uninformed agents believe it is possible that the sensitivity is greater than 1.Asymmetric information, common knowledge, eductive learning, rational expectations, rationalizability, volatility.

Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006, pp.1109-1186.

This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.

Effects of diversification among assets in an agent-based market model

We extend to the multi-asset case the framework of a discrete time model of a single asset financial market developed in Ghoulmie et al (2005). In particular, we focus on adaptive agents with threshold behavior allocating their resources among two assets. We explore numerically the effect of this diversification as an additional source of complexity in the financial market and we discuss its destabilizing role. We also point out the relevance of these studies for financial decision making.Comment: 12 pages, 5 figures, accepted for publication in the Proceedings of the Complex Systems II Conference at the Australian National University, 4-7 December 2007, Canberra, ACT Australi

Is more memory in evolutionary selection (de)stabilizing?

We investigate the effects of memory on the stability of evolutionary selection dynamics based on a multi-nomial logit model in an asset pricing model with heterogeneous beliefs. Whether memory is stabilizing or destabilizing depends in general on three key factors: (1) whether or not the weights on past observations are normalized; (2) the ecology of forecasting rules, in particular the average strength of trend extrapolation and the spread in biased forecasts, and (3) whether or not costs for information gathering of economic fundamentals have to be incurred.

Drag-free and attitude control for the GOCE satellite

The paper concerns Drag-Free and Attitude Control of the European satellite Gravity field and steady-state Ocean Circulation Explorer (GOCE) during the science phase. Design has followed Embedded Model Control, where a spacecraft/environment discrete-time model becomes the realtime control core and is interfaced to actuators and sensors via tuneable feedback laws. Drag-free control implies cancelling non-gravitational forces and all torques, leaving the satellite to free fall subject only to gravity. In addition, for reasons of science, the spacecraft must be carefully aligned to the local orbital frame, retrieved from range and rate of a Global Positioning System receiver. Accurate drag-free and attitude control requires proportional and low-noise thrusting, which in turn raises the problem of propellant saving. Six-axis drag-free control is driven by accurate acceleration measurements provided by the mission payload. Their angular components must be combined with the star-tracker attitude so as to compensate accelerometer drift. Simulated results are presented and discusse

Price Tactics For A Turbulent Environment: A Complexity Theory View

This paper proposes that pricing tactics are influenced by the nature of the external environment. It illustrates the pricing tactics suggested for a turbulent, versus a stable, environment, when viewed through a complexity theory lens. A qualitative, case method, using depth interviews, investigated the pricing tactics in four firms to identify the tactics adopted in more successful, versus less successful, firms in turbulent versus stable environments. The results partially confirmed that the use of destabilizing pricing tactics can be helpful in a turbulent market, while stabilizing tactics can be helpful in a stable market. However, the effect of such tactics on business performance was not clear. These findings will benefit marketers by emphasizing a new way to consider future pricing activities. How this approach can assist marketers, and suggestions for further research, are provided. Since businesses and markets are complex adaptive systems, using complexity theory to understand how to cope in turbulent environments is necessary but has not been widely researched. Therefore, this paper can be seen as a foundation for research using complexity theory to better understand pricing tactics in turbulent environments