Rock around the clock: An agent-based model of low- and high-frequency trading

We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. By contrast, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency traders increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e., their ability to i. generate high bid-ask spreads and ii. synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.     

Innovation,finance, and economic growth: an agent-based approach

This paper extends the endogenous growth agent-based model in Fagiolo and Dosi (Struct Change Econ Dyn 14(3):237–273, 2003) to study the finance–growth nexus. We explore industries where firms produce a homogeneous good using existing technologies, perform R&D activities to introduce new techniques, and imitate the most productive practices. Unlike the original model, we assume that both exploration and imitation require resources provided by banks, which pool agent savings and finance new projects via loans. We find that banking activity has a positive impact on growth. However, excessive financialization can hamper growth. Indeed, we find a significant and robust inverted U-shaped relation between financial depth and growth. Overall, our results stress the fundamental (and still poorly understood) role played by innovation in the finance–growth nexus.

     

The microfoundations of business cycles: an evolutionary, multi-agent model

This work presents an evolutionary model of output and investment dynamics yielding endogenous business cycles. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature on investment patterns, we assume that firms’ investment decisions are lumpy and constrained by their financial structure. Simulation results show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics.     

Fiscal Policies and Credit Regimes: A TVAR Approach

This work investigates how the state of credit markets affects the impact of fiscal policies. We estimate a threshold vector autoregression (TVAR) model on US quarterly data for the period 1984–2010. We employ the spread between BAA‐rated corporate bond yield and 10‐year treasury constant maturity rate as a proxy for credit conditions. We find that the response of output to fiscal policy shocks is stronger and more persistent when the economy is in the ‘tight’ credit regime. Fiscal multipliers are significantly different in the two regimes: they are abundantly and persistently higher than one when firms face increasing financing costs, whereas they are feebler and often lower than one in the ‘normal’ credit regime. The results appear to be robust to different model specifications, fiscal foresight, alternative threshold variables, different measure of variables and sample periods. Copyright © 2014 John Wiley & Sons, Ltd.     

Are output growth‐rate distributions fat‐tailed? some evidence from OECD countries

This work explores some distributional properties of aggregate output growth‐rate time series. We show that, in the majority of OECD countries, output growth‐rate distributions are well approximated by symmetric exponential power densities with tails much fatter than those of a Gaussian (but with finite moments of any order). Fat tails robustly emerge in output growth rates independently of: (i) the way we measure aggregate output; (ii) the family of densities employed in the estimation; (iii) the length of time lags used to compute growth rates. We also show that fat tails still characterize output growth‐rate distributions even after one washes away outliers, autocorrelation and heteroscedasticity. Copyright © 2008 John Wiley & Sons, Ltd.     

The Irresistible Fetish of Utility Theory: From “Pleasure and Pain” to Rationalising Torture

This note briefly examines the path that the economics discipline has taken since adopting the assumption that human behaviour is exclusively driven by utility maximisation. This view has prevented the full comprehension of most economic phenomena and has spread to other social disciplines, occasionally leading to results either ridiculous or tragic, such as using utility theory to rationalise torture. Such academic absurdity must be ended.