Minority Games, Local Interactions, and Endogenous Networks

In this paper we study a local version of the Minority Game where agents are placed on the nodes of a directed graph. Agents care about being in the minority of the group of agents they are currently linked to and employ myopic best-reply rules to choose their next-period state. We show that, in this benchmark case, the smaller the size of local networks, the larger long-run population-average payoffs. We then explore the collective behavior of the system when agents can: (i) assign weights to each link they hold and modify them over time in response to payoff signals; (ii) delete badly-performing links (i.e. opponents) and replace them with randomly chosen ones. Simulations suggest that, when agents are allowed to weight links but cannot delete/replace them, the system self-organizes into networked clusters which attain very high payoff values. These clustered configurations are not stable and can be easily disrupted, generating huge subsequent payoff drops. If however agents can (and are sufficiently willing to) discard badly performing connections, the system quickly converges to stable states where all agents get the highest payoff, independently of the size of the networks initially in place.Minority Games, Local Interactions, Endogenous Networks, Adaptive Agents

Revisiting the predictability of bond risk premia

This paper investigates the source of predictability of bond risk premia by means of long-term forward interest rates. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. We show that the predictive ability of forward rates could be due to the high serial correlation and cross-correlation of bond prices. After a simple reparametrization of models used to predict spot rates or excess returns, we find that forward rates exhibit much less predictive power than previously recorded. Furthermore, our economic value analysis indicates that there are no economic gains to mean-variance investors who use the predictions of these models in a stylized dynamic asset allocation strategy.Bonds ; Bond market ; Risk

Endogenous Networks in Random Population Games

Population learning in dynamic economies has been traditionally studied in over-simplified settings where payoff landscapes are very smooth. Indeed, in these models, all agents play the same bilateral stage-game against any opponent and stage-game payoffs reflect very simple strategic situations (e.g. coordination). In this paper, we address a preliminary investigation of dynamic population games over `rugged' landscapes, where agents face a strong uncertainty about expected payoffs from bilateral interactions. We propose a simple model where individual payoffs from playing a binary action against everyone else are distributed as a i.i.d. U[0,1] r.v.. We call this setting a `random population game' and we study population adaptation over time when agents can update both actions and partners using deterministic, myopic, best reply rules. We assume that agents evaluate payoffs associated to networks where an agent is not linked with everyone else by using simple rules (i.e. statistics) computed on the distributions of payoffs associated to all possible action combinations performed by agents outside the interaction set. We investigate the long-run properties of the system by means of computer simulations. We show that: (i) allowing for endogenous networks implies higher average payoff as compared to "frozen" networks; (ii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s) or not; (iii) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.Dynamic Population Games, Bounded Rationality, Endogenous Networks, Fitness Landscapes, Evolutionary Environments, Adaptive Expectations.

Dynamic Models of Segregation in Small-World Networks

Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by one- or two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We generalize Schelling's model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling's original results seem to be robust to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Small worlds, Scale-free networks, Best-response dynamics

Segregation in Networks.

Schelling (1969, 1971a,b, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact ’locally’ in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling’s original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.

Dynamic Models of Segregation in Small-World Networks

Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by one- or two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We gen- eralize Schelling's model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling's original results seem to be robust to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Small worlds, Scale-free networks, Best-response dynamics

The empirical failure of the expectations hypothesis of the term structure of bond yields

This paper tests the expectations hypothesis (EH) using US monthly data for bond yields spanning the 1952-2003 sample period and ranging in maturity from 1 month to 10 years. We apply the Lagrange multiplier test developed by Bekaert and Hodrick (2001) and extend it to increase the test power: (a) by introducing economic variables as conditioning information; and (b) by using more than two bond yields in the model and testing the EH jointly on more than one pair of yields. While the conventional bivariate procedure provides mixed results, the more powerful testing procedures suggest rejection of the EH throughout the maturity spectrum examined. ; Earlier titles: Testing the expectations hypothesis: some new evidence, New evidence on the expectations hypothesis of the term structure of bond yieldsRational expectations (Economic theory)

Monetary Policy Rules, Asset Prices and Exchange Rates

We examine empirically whether asset prices and exchange rates may be admitted into a standard interest rate rule, using data for the US, the UK and Japan since 1979. Asset prices and exchange rates can be employed as information variables for a standard ‘Taylor-type’ rule or as arguments in an augmented interest rate rule. Our empirical evidence, based on measures of the output gap proxied by marginal costs calculations, suggests that monetary policy-makers may use asset prices and exchange rates not only as part of their information set for setting interest rates, but also to set interest rates to offset deviations of asset prices or exchange rates from their equilibrium levels. These results are open to several alternative interpretations.Asset prices; exchange rates; interest rate rules; monetary policy.