Gabriele Tedeschi

Bankruptcy Cascades in Interbank Markets

We study a credit network and, in particular, an interbank system with an agent-based model. To understand the relationship between business cycles and cascades of bankruptcies, we model a three-sector economy with goods, credit and interbank market. In the interbank market, the participating banks share the risk of bad debits, which may potentially spread a bank’s liquidity problems through the network of banks. Our agent-based model sheds light on the correlation between bankruptcy cascades and the endogenous economic cycle of booms and recessions. It also demonstrates the serious trade-off between, on the one hand, reducing risks of individual banks by sharing them and, on the other hand, creating systemic risks through credit-related interlinkages of banks. As a result of our study, the dynamics underlying the meltdown of financial markets in 2008 becomes much better understandable.

The dynamic of innovation networks: a switching model on technological change

In this paper, we introduce an agent-based model where heterogeneous firms compare and modify their innovation strategies, so generating an evolving network structure. By implementing dynamic behavioral switching via a fitness mechanism based on agents’ performance, companies can endogenously modify their tactics for technological change and switch among three groups: stand-alone innovators, collaborative innovators and imitators. On the one hand, we study the properties of the emerging networks and we show that they reproduce the stylized facts of innovation networks. Moreover, we focus the analysis on the impact of these three innovation categories on the macro economic aggregate, finding that collaborative companies are those having the highest positive impact on the economic system. On the other hand, we use the model to study the effect of different economic innovation policies in increasing macroeconomic performance.

The Boulogne fish market: the social structure and the role of loyalty

We examine the Boulogne wholesale fish market, analysing the structure of the trading network between sellers and buyers. Differently from other works in the literature, our analysis indicates a significant amount of ‘bilateral loyalty’ seller–buyer. Loyalty is from buyers to a few sellers as well as from sellers to a few buyers. We also show that loyalty has an impact on prices, discriminating among agents.

Can Negative Interest Rates Really Affect Option Pricing? Empirical Evidence from an Explicitly Solvable Stochastic Volatility Model

The profound financial crisis generated by the collapse of Lehman Brothers and the European sovereign debt crisis in 2011 have caused negative values of government bond yields both in the U.S.A. and in the EURO area. This paper investigates whether the use of models which allow for negative interest rates can improve option pricing and implied volatility forecasting. This is done with special attention to foreign exchange and index options. To this end, we carried out an empirical analysis on the prices of call and put options on the U.S. S&P 500 index and Eurodollar futures using a generalization of the Heston model in the stochastic interest rate framework. Specifically, the dynamics of the option’s underlying asset is described by two factors: a stochastic variance and a stochastic interest rate. The volatility is not allowed to be negative but the interest rate is. Explicit formulas for the transition probability density function and moments are derived. These formulas are used to estimate the model parameters efficiently. Three empirical analyses are illustrated. The first two show that the use of models which allow for negative interest rates can efficiently reproduce implied volatility and forecast option prices (i.e., S&P index and foreign exchange options). The last studies how the U.S. three-month government bond yield affects the U.S. S&P 500 index.

A Networked Economy: A Survey on the Effect of Interaction in Credit Markets

The aim of this chapter is to review the literature on credit market models by emphasizing the mechanisms able to generate financial crises and contagion. From the theoretical microeconomic literature up to network theory and agent-based methodologies, we illustrate how these different approaches investigate the (in)stability of financial systems. Although very different, these methodologies emphasize the importance of a careful analysis of the interaction among heterogeneous agents, which is recognized as the key element in explaining real and financial cycles.

From bond yield to macroeconomic instability: A parsimonious affine model

We present a hybrid Heston model with a common stochastic volatility to describe government bond yield dynamics. The model is analytically tractable and, therefore, can be efficiently estimated using the maximum likelihood approach and a specific expansion in order to cope with the curse of dimensionality. Twofold is the model contribution. First, it captures changes in the yield volatility and predict future yield values of Germany, French, Italy and Spain. The result is an early-warning indicator which anticipates phases of instability characterizing the time series investigated. Then, the model describes convergence/divergence phenomena among European government bond yields and explores the countries’ reactions to a common monetary policy described through the EONIA interbank rate. We also investigate the potential of this indicator on U.S. data (treasury bills).

Modeling Financial Markets in an Agent-Based Framework

In this chapter we present a stylized financial agent-based model able to reproduce some important regularities emerging in financial time series . Following Tedeschi et al. (Eur Phys J B, 71(4), 489–497, 2009) and Tedeschi et al. (J Econ Behav Organ, 81(1), 82–96, 2012c), we show how an expectation feedback system can produce synchronization effects generating large fluctuations in returns .

Getting Started: The Aggregation Conundrums and Basic Toolkits of Agent Based Modeling

From the very beginning macroeconomics analysis tried to understand aggregate relationships (GDP, Consumption, Investments, etc.). The most famous one is the Keynesian relation between aggregate consumption at time t, \(C_t\) and aggregate income, \(Y_t\)

Herding effects in order driven markets: The rise and fall of gurus

\begin{aligned} C_t = F(Y_t) = \bar{C} + c Y_t \end{aligned}