This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.

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Heterogeneous Agent Models in Economics and Finance

We find strong evidence that three key dimensions of national culture (trust, hierarchy, and individualism) affect merger volume and synergy gains. The volume of cross-border mergers is lower when countries are more culturally distant. In addition, greater cultural distance in trust and individualism leads to lower combined announcement returns. These findings are robust to year and country-level fixed effects, time-varying country-pair and deal-level variables, as well as instrumental variables for cultural differences based on genetic and somatic differences. The results are the first large-scale evidence that cultural differences have substantial impacts on multiple aspects of cross-border mergers.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1968987_code999292.pdf?abstractid=1545514&mirid=3

Lost in Translation? The Effect of Cultural Values on Mergers Around the World

Technological paradigms, regimes and trajectories: Manufacturing and service industries in a new taxonomy of sectoral patterns of innovation

Author(s): Branch, William | Abstract: Previous work with survey data casts doubt on the Rational Expectations Hypothesis. In this paper, we develop a model of expectation formation where agents form their forecasts of inflation by selecting a predictor function from a set of costly alternatives whereby they may rationally choose a method other than the most accurate. We use this model to test whether survey data exhibit rationally heterogeneous expectations. Maximum likelihood is applied to a new discrete choice setting where the observed variable is continuous and the latent variable is discrete. The results show there is dynamic switching that depends on relative mean squared errors of the predictor.

/pdf/the-theory-of-rationally-heterogeneous-expectations-evidence-3gf53ykh1a.pdf

The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations†

https://www.federalreserve.gov/events/conferences/irfmp2010/papers/HommesHEHJan2010.pdf

The heterogeneous expectations hypothesis: Some evidence from the lab

https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/vwl_wirtschaftspolitik/Team/Westerhoff/Publications/2011/JEDC_RF_FW_Fin.pdf

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

In chapter 2 we have considered a monetary model with completely flexible prices and in chapter 3 contrasted this model type with a fixed price model where quantities rather than prices adjusted in order to clear the markets for the domestically produced and the foreign good. The flexprice approach may be considered the earliest in the study of international trade and balance of payments adjustments, while the Keynesian international multiplier analysis was of course only developed after the appearance of Keynes ‘General Theory’ in 1936 and in particular after World War II. The monetary model had a brief renaissance in the early 1970s when the Bretton Woods system came to an end. It was supposed to provide an alternative to the prevailing fixprice macrotheory of Mundell-Fleming type and proposed the establishment of a system of flexible exchange rates. Yet, exchange rates proved to be much more volatile after the breakdown of the Bretton Woods system in 1973 than was predicted by the monetary model. Confronted with such findings the immediate success of another model type, the Dornbusch (1976) model of sticky, but not fixed prices and flexible, in fact too volatile exchange rates in an environment of high capital mobility, can easily be understood.1 The seminal Dornbusch (1976) model2 combines stock and flow equilibria on the asset markets (money, domestic and foreign bonds respectively) with sluggishly adjusting prices on the market for goods and in later extensions also on the market for labor. Money supply shocks thereby enforce overreaction of the domestic interest rate and as a consequence also of the exchange rate, with respect to their new steady state values, and only after some time are eliminated by subsequent convergence to their (new) steady state values. In appropriately revised form the Dornbusch approach can be viewed to be composed of short-run Mundell-Fleming elements, a new type of exchange rate dynamics, and long-run Classical features (neutrality of money and the relative form of the PPP). It therefore synthesizes to some extent the three preceding chapters of this part of the book by providing an IS-LM-PC approach to the dynamics of exchange rates and expectations of exchange rate depreciation or appreciation with a Classical long-run outlook.

Exchange Rate Volatility

Applying the method of simulated moments to estimate a small agent-based asset pricing model☆

The paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model’s goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.

/pdf/why-a-simple-herding-model-may-generate-the-stylized-facts-4gbdf4733t.pdf

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

The paper estimates an elementary agent-based financial market model recently put forward by the same authors. Invoking the two trader types of fundamentalists and chartists, it comprises four features: price determination by excess demand; a herding mechanism that gives rise to a macroscopic adjustment equation for the population shares of the two groups; a rush towards fundamentalism when the price misalignment becomes too large; and, finally, differently strong noise components in the demand per chartist and fundamentalist trader, which implies a structural stochastic volatility in the returns. The estimation is performed using the method of simulated moments. Combining it with bootstrap and Monte Carlo methods, it is found that the model cannot be rejected by the empirical daily returns from a stock market index and a foreign exchange rate. Measures of the matching of the single moments are satisfactory, too, while the behavioural parameters are well identified and are able to discriminate between the two markets.

/pdf/estimation-of-a-structural-stochastic-volatility-model-of-5f4px311je.pdf

Reiner Franke

Papers

Structural stochastic volatility in asset pricing dynamics: Estimation and model contest

Exchange Rate Volatility

Applying the method of simulated moments to estimate a small agent-based asset pricing model☆

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

Estimation of a Structural Stochastic Volatility Model of Asset Pricing