Prospect Theory in the Heterogeneous Agent Model

Using the Heterogeneous Agent Model framework, we incorporate an extension based on Prospect Theory into a popular agent-based asset pricing model. This extension covers the phenomenon of loss aversion manifested in risk aversion and asymmetric treatment of gains and losses. Using Monte Carlo methods, we investigate behavior and statistical properties of the extended model and assess how our extension is manifested in different strategies. We show that, on the one hand, the Prospect Theory extension keeps the essential underlying mechanics of the model intact, but on the other hand it considerably changes the model dynamics. Stability of the model is increased and fundamentalists may be able to survive in the market more easily. When only the fundamentalists are loss-averse, other strategies profit more.

     

Trade credit and supplier competition

This paper examines how competition among suppliers affects their willingness to provide trade credit financing. Trade credit extended by a supplier to a cash constrained retailer allows the latter to increase cash purchases from its other suppliers, leading to a free rider problem. A supplier that represents a smaller share of the retailer’s purchases internalizes a smaller part of the benefit from increased spending by the retailer and, as a result, extends less trade credit relative to its sales. In consequence, retailers with dispersed suppliers obtain less trade credit than those whose suppliers are more concentrated. The free rider problem is especially detrimental to a trade creditor when the free-riding suppliers are its product market competitors, leading to a negative relation between product substitutability among suppliers to a given retailer and trade credit that the former provide to the latter. We test the model using both simulated and real data. The estimated relations are consistent with the model’s predictions and are statistically and economically significant.     

Consumer Power: Evolution in the Digital Age

The predictions of growing consumer power in the digital age that predated the turn of the century were fueled by the rise of the Internet, then reignited by social media. This article explores the intersection of consumer behavior and digital media by clearly defining consumer power and empowerment in Internet and social media contexts and by presenting a theoretical framework of four distinct consumer power sources: demand-, information-, network-, and crowd-based power. Furthermore, we highlight technology's evolutionary role in the development of these power sources and discuss the nature of shifts in power from marketers to consumers in terms of each source. The framework organizes prior marketing literature on Internet-enabled consumer empowerment and highlights gaps in current research. Specific research questions are elaborated for each source of power outlining the agenda for future research areas.     

Credit Support for Export: Robust Evidence from the Czech Republic

The topic of this paper is quite a novel one – it is one of few empirical academic papers dealing with export credit. Moreover, it is the first analysis of this kind which focuses on transition economies. The paper deals with export credit promotion in the Czech Republic. The development and structure of Czech trade and export support is presented first, followed by an econometric analysis of the gravity model of Czech Republic trade. A panel of 160 countries in 1996–2008 is analysed and two gravity models of exports for the Czech Republic are estimated, the static model by fixed effects (LSDV estimator) and the dynamic model by System GMM. Due to ambiguous conclusions we assume that the behaviour of our explanatory variables is not uniform and our data set behaves as a mixture of countries with heterogeneous behaviour. This means that traditional techniques of estimation which include all observations into one model do not give significant results. Thus, we use robust techniques of estimation that solve the problem of heterogeneous patterns in data sets. Out of several possibilities we use the Least Trimmed Squares estimator (LTS) with a leverage point. We show that guarantees are a significant factor that influences positively the volume of exports in the Czech Republic. Moreover, there exist more variables that affect the size of exports in the Czech Republic. Market forces described by GDP, distance, political risk or gross fix capital formation are significant in our econometric model. We find that higher GDP, shorter distance or lower political risk have a positive impact on Czech exports.     

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

This paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe theory on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology through simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of US stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behaviour in the second half. Translating the results, we find that the US stock market’s downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during the second half of the period, exogenous forces seem to be driving the market’s instability. The results suggest that the proposed methodology provides an important shift in the application of catastrophe theory to stock markets.     

Combining revealed and stated preferences data

Our objective is to develop a unifying framework for the incorporation of different types of survey data in individual choice models. We describe statistical methodologies that combine multiple sources of data in the estimation of individual choice models and summarize the current state of the art of data combination methods that have been used with market research data. The most successful applications so far have combined revealed and stated preference data. We discuss different types of market and survey data and provide examples of research contexts in which one might wish to combine them. Although these methods show a great deal of promise and have already been used successfully in a number of applications, several important research issues remain. A discussion of these issues and directions for further research conclude the paper.