Simulation Analysis as a Way to Assess the Performance of Important Unit Root and Change in Persistence Tests

This chapter shows a way to, using simulation analysis, assess the performance of some of the most popular unit root and change in persistence tests. The authors do this by means of Monte Carlo simulations. The findings suggest that these tests show a lower than expected performance when dealing with some of the processes commonly believed to be found in the economic and financial data. The output signals that extreme care should be taken when trying to support a theory using real data. As the results show, a blind practitioner could get misleading implications almost surely. As an empirical exercise, the authors show that the considered test finds evidence of a unit root process in the US house price index. Nonetheless, as the simulation analysis shows, extreme caution should be taken when analyzing these results.

Optimal Patent Design

Patents provide a patentee with a degree of monopoly power over a region of product space. The “breadth” and “duration” of patents are policy choices. Increasing patent breadth and duration will ceteris paribus increase the rent, which an individual inventor could earn from a commercially successful invention. However, the precise nature of the relationship between patent policy and the rate of societal technical advance, which is stimulated by a given patent design, is not well understood. In this chapter, the authors novelly investigate this issue using an agent-based modeling approach. The simulation results obtained raise questions about the real utility of patent policy in promoting technological advance and suggest that other policy instruments are actually more important.

Preparing Simulations in Large Value Payment Systems using Historical Data

Simulations in large value payment systems have become a common tool for stress scenario analyses, often using historical data. The reason for simulating is that disruptions in payment systems are not very common. Simulation of realistic scenarios requires adequate preparation. As part of the preparation, it is essential 1) to have a thorough understanding of the structure of the investigated market, 2) to potentially remove certain types of transactions, such as funding-related transactions (interbank loans), and 3) to understand how banks react to a shock. The financial crisis starting in the summer of 2007 caused several stressful events worldwide and provided insight into how banks behaved during these events.

The Use of Simulations as an Analytical Tool for Payment Systems

Simulations are among the analytical tools in payment systems analysis. They can be used to overcome epistemological weaknesses of models and calibrations, and they are virtual experiments that do not affect the real performance of payment systems. The chapter is intended to give an inside view into the use of simulations as an analysis tool for payment systems as well as settlement systems. Section 1 highlights the basic features of payment systems in order to explain the usefulness of, and the most important questions addressed by, simulations. Based on these features, an epistemological assessment of simulations versus other analytical tools will show the range and limits of simulations (section 2). Following the historic development, the use of simulations for functional development will be explained in section 3, before dealing with oversight aspects (section 4). Finally, in section 5, the authors list a couple of practical tools to do simulations and to discuss tendencies in simulation tools and speculate on the future direction of research.

Agent-Based Modeling of the El Farol Bar Problem

In this chapter, the authors study the self-coordination problem as demonstrated by the well-known El Farol problem (Arthur, 1994), which has become what is known as the minority game in the econophysics community. While the El Farol problem or the minority game has been studied for almost two decades, existing studies are mostly only concerned with efficiency. The equality issue, however, has been largely neglected. In this chapter, the authors build an agent-based model to study both efficiency and equality and ask whether a decentralized society can ever possibly self-coordinate a result with the highest efficiency while also maintaining the highest degree of equality. The agent-based model shows the possibility of achieving this social optimum. The two key determinants to make this happen are social preferences and social networks. Hence, not only do institutions (networks) matter, but individual characteristics (preferences) also matter. The latter part are open to human-subject experiments for further examination.

Modeling the FX Market Traders’ Behavior

In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties (stylized facts) of the traders’ trading behavior in the FX market using a high-frequency dataset of anonymised OANDA individual traders’ historical transactions on an account level spanning 2.25 years. Using the identified stylized facts of real FX market traders’ behavior, the authors evaluate the collective behavior of the trading agents in resembling the collective behavior of the FX market traders. The study identifies the conditions under which the stylized facts of trading agents’ collective behaviors resemble those for the real FX market traders’ collective behavior. The authors perform an exploration of the market’s features in order to identify the conditions under which the stylized facts emerge.

Systemic Risk, Stress Testing, and Financial Contagion

By using the proposed framework, it is also possible to perform stress testing in a coherent way, including second round effects like contagion through the interbank market. Additionally, it is possible to follow the evolution of certain coherent risk measures, like the CVaR, in order to evaluate if the system is becoming more or less risky, in fact, more or less fragile. Additionally, the authors decompose the distribution of losses of the whole banking system into the systemic and the contagion elements and determine if the system is more prone to experience contagious difficulties during a certain period of time.

Managing Intraday Liquidity

The present chapter calculates the liquidity usage of the Mexican Real Time Settlement Payment System, SPEI, during a one month period. In particular, the authors’ interest is to give insight about how payment systems participants manage the settlement of low and large value payment transactions in real-time. To that end, they create an artificial environment, in which they use historical data from April 7 to May 7, 2010, and reproduce the operational conditions of SPEI. All payment instructions that arrived from 9:00 a.m. to 17:00 p.m. each working day are included. For each of these days, the authors arrange the transactions in four sets, delimited according to their value: all payments; payment orders with value higher than 100,000 MXN; transactions with value higher than 1,000,000 MXN; and payments with value higher than 10,000,000 MXN. The authors use a simulation environment to reproduce SPEI’s operational conditions in order to measure the liquidity usage in different settings of settlement speed requirements.

Simulation Approaches to Risk, Efficiency, and Liquidity Usage in Payment Systems

Payment systems constitute a critical aspect of modern economic infrastructure; yet understanding the payment system mechanisms remains elusive in the face of rapidly evolving financial markets and intricate institutional linkages. Computer simulations of payment systems have proven useful in determining optimal balances of risk, efficiency, and liquidity usage. Constructs such as gridlock-resolution algorithms and liquidity-saving mechanisms are now routinely applied in such areas as optimization of liquidity and payment delay, but can also be used to assess potential impacts of changes in policy or system setups. In addition, simulations can be extended to incorporate behavioral elements of participants by modeling their behavior with Agent-Based Modeling (ABM). The 2008 global financial crisis has increased interest in simulations to identify and quantify risk, particularly where new channels of contagion and complex interlinkages of markets and payment systems are involved. Payment system simulations offer central bank authorities broad possibilities to improve their risk monitoring and should be incorporated as a standard part of financial stability analysis.

What Matters in Determining Capital Surcharge for Systemically Important Financial Institutions?

One way of internalising the externalities each individual bank imposes on the rest of the financial system is to impose capital surcharges (KS) on them in line with their systemic importance. Given the complexity of the financial system and the resulting difficulties in measuring systemic importance, it is sometimes argued to simply apply higher KS to larger banks, abstracting from other factors like interconnectedness. In this chapter, the authors consider different network structures of the banking system that are characterized by two different centrality measures. Their main finding is that size alone is not always a good proxy for systemic importance and must be supplemented with detailed information on interbank exposures. A relatively small bank playing an outsized role in the interbank market might be more systemic, and thus garner a higher capital surcharge, than a less connected bank of somewhat larger size. Alternatively, if the centrality of banks in an interbank network is positively correlated with their size, then proxies of a bank’s systemic importance largely based on size are sufficient indicators.