Blue Flower

Project "PN-II-ID-PCE-2011-3-1054 - Uncertainty, Complexity and Financial Stability"



The general objective of the project is reaching a better knowledge of the structure, behaviour and functionality of the Financial System, as well as a better substantiation of the concept of Systemic Risk and of the methods for its quantification. This will contribute to a better understanding of the role of the Financial System in the functioning of a modern economy and of its contribution to the process of economic growth. At the same time, the results expected to be obtained will contribute to a better substantiation of the regulations that are conceived worldwide in order to prevent future financial crises.
Taking into account the present stage of knowledge in the field, the increased complexity of economic-financial phenomena, generated by the globalization process, by the development of science and technology, by the deepening of capital markets and their interconnection, we intend to achieve within the project the following specific objectives:


1. A new understanding of how financial markets work and a new paradigm for asset pricing

The key departure from conventional theory is to recognise that investors do not invest directly in securities but through agents such as fund managers. Agents have better information and different objectives than their customers (principals) and this asymmetry is shown as the source of inefficiency - mispricing, bubbles and crashes. A separate outcome is that agents are in a position to capture for themselves the bulk of the returns from financial innovations. Principal/agent problems do a good job of explaining how the global finance sector has become so bloated, profitable and prone to crisis. Remedial action involves the principals changing the way they contract with, and instruct agents. The chapter ends with a manifesto of policies that pension funds and other large investors can adopt to mitigate the destructive features of delegation both for their individual benefit and to promote social welfare in the form of a leaner, more efficient and more stable finance sector.
Prevailing theory asserts that asset prices are informationally efficient and that capital markets are self-correcting. It also treats the finance sector as an efficient passthrough, ignoring the role played by financial intermediaries in both asset pricing and the macro-economy. The evidence of the past decade has served to discredit the basic tenets of finance theory. Given that banking and finance are now seen as a source of systemic instability, the wisdom of ignoring the role of financial intermediaries has been called into question.
This chapter advances an alternative paradigm which seems to do a better job of explaining reality. Its key departure from mainstream theory is to incorporate delegation by principals to agents. The principals in this case are the end-investors and customers who subcontract financial tasks to agents such as banks, fund managers, brokers and other specialists. Delegation creates an incentive problem insofar as the agents have more and better information than their principals and because the interests of the two are rarely aligned.
Asymmetric information has been partially explored in corporate finance and banking but hardly at all in asset pricing which is arguably the central building block in finance. Incorporating delegation permits the retention of the assumption of rational expectations which, in turn, makes it possible to keep much of the existing formal. Delegation creates an agency problem. Agents have access to more and better information than the investors who appoint them, and the interests and objectives of agents frequently differ from those of their principals. For their part, principals cannot be certain of the competence or diligence of the agents.
Introducing agents brings greater realism to asset-pricing models and, more importantly, gives a far better understanding of how capital markets function. Importantly, this is achieved whilst maintaining the assumption of fully rational behaviour by all participants. Models incorporating agents have more working parts and therefore a higher level of complexity, but the effort is richly rewarded by the scope and relevance of the predictions framework of finance. Introducing agents both transforms the analysis and helps explaining many aspects of mispricing and other distortions that have relied until now upon behavioural assumptions of psychological bias.

2. Integrating the Financial System in a DSGE Model with agents who are averse not only to risk, but also to ambiguity (Knightian uncertainty)

The standard framework of quantitative macroeconomics is based on expected utility preferences and rational expectations. Changes in uncertainty are typically modeled as expected and realized  changes in risk. An increase in uncertainty is described by the expected increase in a measure of risk (for example, the conditional volatility of a shock or the probability of a disaster). Moreover, rational expectations imply that agents' beliefs coincide with those of the econometrician (or model builder).
We intend to explore the impact of credit supply factors in business cycle fluctuations using a dynamic stochastic general equilibrium (DSGE) model with financial frictions enriched with an imperfectly competitive banking sector and with agents who are averse to ambiguity (Knightian uncertainty). Ambiguity averse agents do not think in terms of probabilities since they lack the confidence to assign probabilities to all relevant events. An increase in uncertainty may then correspond to a loss of confidence that makes it more difficult to assign probabilities. Formally, the preferences are described using “multiple priors” utility (Gilboa and Schmeidler, 1989). Agents act as if they evaluate plans using a worst case probability drawn from a set of multiple beliefs. A loss of confidence is captured by an increase in the set of beliefs. It could be triggered, for example, by worrisome news about the future. Agents respond to a loss of confidence as their worst case probability changes.
In the proposed DSGE model with financial frictions, banks issue collateralized loans to both households and firms, obtain funding via deposits, and accumulate capital out of retained earnings. Loan margins depend on the banks’ capital-to-assets ratio and on the degree of interest rate stickiness. Balance-sheet constraints establish a link between the business cycle, which affects bank profits and thus capital, and the supply and cost of loans. The model is estimated with Bayesian techniques using data for the euro area. In modeling credit supply, we add a stylized banking sector to a model with credit frictions and borrowing constraints 'a la Iacoviello (2005).
Banks have three distinctive features. First, they enjoy some degree of market power in loan and deposit markets and set different rates for households and firms. We do not try to pinpoint the source of market power, which the theoretical literature has typically linked to asymmetric information problems, long-term customer relationships, or the presence of switching costs; instead, we calibrate the average elasticities of loan and deposit demands to reproduce the degree of market power observed in the euro area. Second, banks face costs of adjusting retail rates and the passthrough on loan and deposit rates of changes in the policy rate is incomplete on impact. This is an important ingredient if the model is to capture the different speeds at which banks’ rates react to changes in monetary conditions: the empirical evidence in favor of a partial and heterogenous adjustment of bank rates in the euro area is, indeed, overwhelming.
In order to incorporate ambiguity and shocks to confidence into the DSGE model, agents' set of beliefs can be parameterized by an interval of means for exogenous shocks captured the model. A loss of confidence is captured by an increase in the width of such the interval, therefore an increase in ambiguity. A shock to confidence thus works like a news shock: an agent who loses confidence responds as if he had received bad news about the future. The difference between a loss of confidence and bad news is that the former is not necessarily followed, on average, by the realization of a bad outcome.