PUBLICATIONS

We construct a model of cyclical growth with agent-based features designed to study the network origins of aggregate fluctuations from a demand-side perspective. In our model, aggregate fluctuations result from variations in investment behaviour at firm level motivated by endogenously-generated changes in ‘animal spirits’. In addition to being influenced by their own economic conditions, firms pay attention to the performance of first-degree network neighbours, weighted (to differing degrees) by the centrality of these neighbours in the network, when revising their animal spirits. This allows us to analyse the effects of the centrality of linked network neighbours on the amplitude of aggregate fluctuations. We show that the amplitude of fluctuations is significantly affected by the eigenvector centrality, and the weight attached to the eigenvector centrality, of linked network neighbours. The dispersion of this effect about its mean is shown to be similarly important, resulting in the possibility that network properties can result in ‘great moderations’ giving way to

sudden increases in the volatility of aggregate economic performance.

The aim of this paper is to provide a theoretical analysis of the role of social conventions as emergent phenomena in financial markets, the latter being thought of as dynamically complex systems. Combining complexity and reflexivity with Keynes’s view of financial markets, we develop a ‘convention-based’ approach which shows how conventions can only temporarily stabilize the system, inevitably leading to financial instability and crises. Then, we adopt this framework to investigate a central banking agenda to act ‘against the tide’ (i.e., the prevailing convention) in the build-up of a speculative bubble, which calls for macroprudential policy and financial regulation. In this respect, we analyze the moral suasion channel that allows the central monetary authority to ‘talk down’ the market through asset-price management and the unconventional monetary policy toolkit.

Keynes’ ‘fallacy of composition’ and, later, the ‘Santa Fe perspective’ have shown that the economy may be defined as a “complex adaptive system”, since it is not something given but results from constantly developing interactions among heterogeneous individuals. Endorsing the definition of ‘dynamic complexity’, the paper analyses the extent to which Keynes’ notion of complexity is coterminous with the one developed at the Santa Fe Institute. An analysis of the role of expectations in the General Theory, along with the dimension of time, history and path-dependence, makes it possible to conceive Keynes’ work as an open, complex evolving system and to establish a direct connection with the ‘Santa Fe perspective’. However, Keynes’ “two-stage methodology” which allows the analyst to deal with complex organic material without resorting to reductionism by means of ordinary language involves the inherent non-measurability of complex magnitudes which does not depend on the individual’s limited ability to measure the complex object. This feature may cast some doubts on the inheritance of Keynes’s methodology by the ‘Santa Fe perspective’, which attempts to handle complexity through simulation techniques implicitly based on a sophisticated mathematical formalism.

In this chapter, we investigate the US stock market dynamics over bull markets, bear markets and corrections through the lenses of statistical equilibrium. By making use of an entropy-constrained framework, we build a theoretical model to recover the cross-sectional distribution of daily returns of individual companies listed on the S&P 500, over the period 1988-2019. The results of the model shed light on the microscopic as well as macroscopic behavior of the stock market, in addition to providing insights in terms of stock returns distribution and investors’ behavior.

We construct a model of cyclical growth with agent-based features designed to study the network origins of aggregate fluctuations from a demand-side perspective. In our model, aggregate fluctuations result from variations in investment behavior at firm level motivated by endogenously-generated changes in `animal spirits' or the state of long run expectations (SOLE). In addition to being influenced by their own economic conditions, firms pay attention to the performance of first-degree network neighbours, weighted (to differing degrees) by the centrality of these neighbours in the network, when revising their SOLE. This allows us to analyze the effects of the centrality of linked network neighbours on the amplitude of aggregate fluctuations. We show that the amplitude of fluctuations is significantly affected by the eigenvector centrality, and the weight attached to the eigenvector centrality, of linked network neighbours. The dispersion of this effect about its mean is shown to be similarly important, resulting in the possibility that network properties can result in `great moderations' giving way to sudden increases in the volatility of aggregate economic performance.

This paper attempts to develop a theory of statistical equilibrium based on an entropy-constrained framework, that allow us to explain the distribution of stock returns over different market trends. By making use of the Quantal Response Statistical Equilibrium model (Scharfenaker and Foley, 2017), we recover the cross-sectional distribution of daily returns of individual companies listed the S&P 500, over the period 1988-2019. We then make inferences on the frequency distributions of returns by studying them over bull markets, bear markets and corrections. The results of the model shed light on the microscopic as well as macroscopic behavior of the stock market, in addition to providing insights in terms of stock returns distribution.

Ever since the 2008 financial crisis, there has been both a widespread recognition that the mainstream approach on financial markets has failed to anticipate and to justify the crisis and on the need of ex ante and ex post adequate economic policies to cope with such phenomena. The aim of our paper is to provide a theoretical and methodological analysis of the role of conventions as emergent phenomena in financial markets, the latter being thought of as dynamically complex systems. Drawing upon the notion of ‘dynamic complexity’ and Keynes’ view of financial markets, we claim that social conventions can only provisionally stabilize the system, but they will eventually lead to financial instability and crisis. Then, we adopt this framework to investigate the implications for monetary policy to stabilize the system by virtue of the role of central bank to intervene, and thus shape, a convention. In this respect, we consider the credibility of the monetary authority and how it can be exerted through ‘moral suasion’ to control the financial fragility of investors’ balance-sheet positions as well as to affect the convention around the longterm interest rate.