A model of network formation for the overnight interbank market: When is core-periphery an illusion?
Mikhail Anufriev, Andrea Deghi, Paolo Pin and Valentyn Panchenko, Journal of Economic Theory, forthcoming.
We develop a theoretical model of network formation in the overnight interbank market, where banks manage liquidity under reserve uncertainty by strategically forming bilateral lending relationships. The model incorporates counterparty risk and the central bank's corridor system, yielding endogenously determined equilibrium networks. A key result, relevant for systemic stability policy, is that the equilibrium network is bipartite: active banks act either as lenders or borrowers, and no strategic (interbank) intermediation arises. We also show that, via temporal aggregation of equilibrium networks, apparent intermediation and a core-periphery structure emerge. We validate these predictions using e-MID market data, showing that the model reconciles the frequency-dependent network features documented in the empirical literature for this market.
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Time pressure reduces financial bubbles: Evidence from a forecasting experiment
Mikhail Anufriev, Frieder Neunhoeffer and Jan Tuinstra, Experimental Economics, forthcoming.
We investigate whether time pressure exacerbates or mitigates bubbles in laboratory experiments. We find that under high time pressure price volatility is lower and market prices are closer to their fundamental value. This is due to participants using simpler adaptive forecasting strategies, instead of the self-reinforcing extrapolative expectations that they use under low time pressure, and which are conducive to the emergence of bubbles. In addition, by substantially increasing the number of decision periods in our experiment we find that in the long run prices eventually tend to converge to their fundamental value, also in the absence of time pressure.
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Learning in Two-Dimensional Beauty Contest Games: Theory and Experimental Evidence
Mikhail Anufriev, John Duffy and Valentyn Panchenko, Journal of Economic Theory, Volume 201, 105417
We extend the beauty contest game to two dimensions: each player chooses two numbers to be as close as possible to certain target values, which are linear functions of the averages of the two number choices. One of the targets depends on the averages of both numbers, making the choices interrelated. We report on an experiment where we vary the eigenvalues of the associated two-dimensional linear system and find that subjects can learn the Pareto-optimal Nash Equilibrium of the system if both eigenvalues are stable and cannot learn it if both eigenvalues are unstable. Interestingly, subjects can also learn it if the system has the saddlepath property – with one stable and one unstable eigenvalue — but only if the one unstable eigenvalue is negative. We show theoretically that our results cannot be explained by homogeneous level-k models where all agents apply the same level k depth of reasoning to their choices, including the naïve learning model. However, our results can be explained by a mixed cognitive-levels model, including the adaptive learning model. We also run a horserace between many models used in the literature with the winner being a simple mixed model with levels 0, 1, and equilibrium reasoning.
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Simple forecasting heuristics that make us smart: Evidence from different market experiments
Mikhail Anufriev, Cars Hommes and Tomasz Makarewicz, Journal of the European Economic Association, Volume 17, Issue 5, Pages 1538–1584, 2019
In this paper we address the question of how individuals form expectations and invent, reinforce, and update their forecasting rules in a complex world. We do so by fitting a novel, parsimonious and empirically validated genetic algorithm learning model with explicit heterogeneity in expectations to a set of laboratory experiments. Agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behavior; (2) explanation of individual and aggregate behavior in three different experimental settings, (3) improved one- and 50-period ahead forecasting of experiments, and (4) characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Models.
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Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments
Mikhail Anufriev and Cars Hommes, American Economic Journal: Microeconomics, Vol. 4 (4), pp. 35-64, 2012.
In recent “learning to forecast” experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior.
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