Introduction to internally consistent modeling,aggregation, inference,and policy

This special issue of the Journal of Econometrics honors William A. Barnett’s exceptional contributions to unifying economic theory with rigorous statistical inference to interpret economic data and inform public policy. It is devoted to papers that advance microeconometrics, macroeconometrics, and financial econometrics to build models to interpret evidence.     

To bail-out or to bail-in? Answers from an agent-based model

Since the beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The question of how to resolve failing financial institutions, and how this depends on economic preconditions, is therefore a pressing and controversial issue of vast political importance. In this work we employ an agent-based model to study the economic and financial ramifications of three highly relevant crisis resolution mechanisms. To establish the validity of the model we show that it reproduces a series of key stylized facts of the financial and real economy. The distressed institution can either be closed via a purchase & assumption transaction, it can be bailed-out using taxpayer money, or it may be bailed-in in a debt-to-equity conversion. We find that for an economy characterized by low unemployment and high productivity the optimal crisis resolution with respect to financial stability and economic productivity is to close the distressed institution. For economies in recession with high unemployment the bail-in tool provides the most efficient crisis resolution mechanism. Under no circumstances do taxpayer-funded bail-out schemes outperform bail-ins with private sector involvement.     

Debt,financial sector development and Asian economic growth

This special issue of Economic Systems includes four papers that broadly relate to the relationship between financial sector development and debt in the Asia-Pacific region. The motivation underlying the choice of the Asia-Pacific region in this special issue is due to the increasing importance that this region will play in powering future world economic growth. In addition, in recent times the focus has been upon the importance of equity and financial sector development, as opposed to debt. The world currently faces many economic imbalances and increasing importance is being placed in the Asia-Pacific upon the breadth and depth of bond markets in helping to restore equilibrium in the world economy. Debt influences corporate funding opportunities and may assist countries in achieving sustainable economic growth. These four papers contribute to our knowledge on the relationship between debt and financial sector development.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Spatial dynamics and metropolitan change

"This special issue presents contributions to a collaborative effort to analyze 'the dynamics of metropolitan processes and policies'....[It] contains four papers which focus on industrial change and economic restructuring; two papers deal with population relocation and migration processes; one paper contains a study of economic cycles in space and one paper treats the assessment of urban investment and urban renewal projects." The geographic focus is on the developed countries.     

Large data sets in finance and marketing: introduction by the special issue editor

On December 18 and 19 of 1997, a small conference on the "Statistical Analysis of Large Data Sets in Business Economics" was organized by the Rotterdam Institute for Business Economic Studies. Eleven presentations were delivered in plenary sessions, which were attended by about 90 participants. The current issue of Statistica Neerlandica contains five papers originating from several of these presentations. All papers have been refereed, and revised according to the suggestions made by the reviewers. It is a great pleasure for me to introduce this issue with remarks on the specific field of interest, a brief discussion of the contents of this issue and acknowledgements. I hope the reader finds the issue useful and stimulating for further research.     

Basel III capital surcharges for G-SIBs are far less effective in managing systemic risk in comparison to network-based,systemic risk-dependent financial transaction taxes

In addition to constraining bilateral exposures of financial institutions, there exist essentially two options for future financial regulation of systemic risk: First, regulation could attempt to reduce the financial fragility of global or domestic systemically important financial institutions (G-SIBs or D-SIBs), as for instance proposed by Basel III. Second, it could focus on strengthening the financial system as a whole by reducing the probability of large-scale cascading events. This can be achieved by re-shaping the topology of financial networks. We use an agent-based model of a financial system and the real economy to study and compare the consequences of these two options. By conducting three computer experiments with the agent-based model we find that re-shaping financial networks is more effective and efficient than reducing financial fragility. Capital surcharges for G-SIBs could reduce systemic risk, but they would have to be substantially larger than those specified in the current Basel III proposal in order to have a measurable impact. This would cause a loss of efficiency.     

On the problem of calibrating an agent based model for financial markets

Agent based models are very widely used in different disciplines. In financial markets, they can be used to explain well known features called stylised facts and fit statistical properties of data. For this reason, they can model price movements better than standard models using gaussianity. Calibration and validation are essential issues in agent-based modeling. However, calibrating such models is not yet sufficiently considered in the literature. In this paper, a Nelder–Mead simplex algorithm coupled with threshold accepting algorithm (Gilli and Winker in Comput Stat Data Anal 42:299–312, 2003) and a genetic algorithm have been implemented to calibrate the model presented by Farmer and Joshi (J Econ Behav Org 49:149–171, 2002) and the outcomes have been compared and discussed. The data used are closing prices of S&P500 Composite index and a particular attention has been devoted to the choice of the objective function.     

Transaction taxes, greed and risk aversion in an agent-based financial market model

Recent agent-based financial market models came to the result that taxing financial transactions does not per se increase financial stability and that the response of volatility and misalignments to rising tax rates seem to be u-shaped. Moreover, greed and the risk appetite of traders are often blamed for financial instability and there is no evidence how greed and risk aversion affect the effectiveness of regulations in financial markets. We aim to add to this gap in the literature by analyzing how the effectiveness of transaction taxes depend on different behavioral patterns within an agent-based framework. Our simulations indicate that a tax rate of 0.1% demarcates the stabilizing tax regime from the destabilizing one. We figure out that transaction taxes are less effective, either when chartists trade more aggressively, fundamentalists trade less aggressively, agents switch more frequently between trading strategies or only have short memory in their fitness measures. Lower risk aversion of agents, however, makes higher tax rates more effective as indicated by a flatter volatility response curve. We conclude that additional regulations should concentrate on the traders’ responsibilities for their risk-exposure.     

Knowledge spillover-based strategic entrepreneurship

Research on knowledge spillovers (KS) has each been the subject of much scholarly attention, but has mostly been considered independently rather than in combination with each other important issue – strategic entrepreneurship (SE). The purpose of this special issue is to integrate these two approaches and to develop theory in addition to empirically investigate the phenomenon inspiring future research on the topic. This paper draws on the knowledge spillover-based strategic entrepreneurship in a multilevel context, examines the logic of the approach, and introduces the special issue by summarizing the papers contributions. Future research suggestions from the papers included in the special journal issue are also discussed and explained.     

Robustness and the general dynamic factor model with infinite-dimensional space: Identification,estimation, and forecasting

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. As second-order models, however, they are sensitive to the presence of outliers—an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al. 2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation, and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical data set of 115 US macroeconomic and financial time series.     

Global imbalances and dynamics of international financial markets

We provide an overview of the special issue “Global Imbalances and dynamics of international financial markets”. This special issue, which is associated with the 7th International Finance Conference, features research papers dealing with the impact of global imbalances, market complexity, and the impact of the recent global financial crisis on the conduct of monetary policies, financial market dynamics, financial stability, and risk management models.     

Simple agent-based dynamical system models for efficient financial markets: Theory and examples

We propose an agent-based framework, based on simple piecewise linear time-invariant continuous-time dynamical systems models, as a means for describing efficient financial markets. We show by examples that many of the common agent-specific trading strategies occurring in the academic literature, including chartists and fundamentalists of various kinds, can be described in the proposed framework. We present definitions for weak and strong market efficiency and provide necessary and sufficient conditions for them to hold. We present minimal examples of strongly and weakly efficient markets to show that these concepts are natural and easy to satisfy in agent-based models, and that the models can reproduce both statistical and behavioral stylized facts of real markets. We provide examples to demonstrate that the framework can be extended for agents with delays in information processing, as well as for agents with time-varying strategies and for nonlinear market impact functions. We also provide a counterexample to show that the proposed market efficiency concepts may require modification in generalizations for nonlinear trading strategies.     

Recent developments in financial economics and econometrics: An overview

Research papers in empirical finance and financial econometrics are among the most widely cited, downloaded and viewed articles in the discipline of Finance. The special issue presents several papers by leading scholars in the field on “Recent Developments in Financial Economics and Econometrics”. The breadth of coverage is substantial, and includes original research and comprehensive review papers on theoretical, empirical and numerical topics in Financial Economics and Econometrics by leading researchers in finance, financial economics, financial econometrics and financial statistics. The purpose of this special issue on “Recent Developments in Financial Economics and Econometrics” is to highlight several novel and significant developments in financial economics and financial econometrics, specifically dynamic price integration in the global gold market, a conditional single index model with local covariates for detecting and evaluating active management, whether the Basel Accord has improved risk management during the global financial crisis, the role of banking regulation in an economy under credit risk and liquidity shock, separating information maximum likelihood estimation of the integrated volatility and covariance with micro-market noise, stress testing correlation matrices for risk management, whether bank relationship matters for corporate risk taking, with evidence from listed firms in Taiwan, pricing options on stocks denominated in different currencies, with theory and illustrations, EVT and tail-risk modelling, with evidence from market indices and volatility series, the economics of data using simple model free volatility in a high frequency world, arbitrage-free implied volatility surfaces for options on single stock futures, the non-uniform pricing effect of employee stock options using quantile regression, nonlinear dynamics and recurrence plots for detecting financial crisis, how news sentiment impacts asset volatility, with evidence from long memory and regime-switching approaches, quantitative evaluation of contingent capital and its applications, high quantiles estimation with Quasi-PORT and DPOT, with an application to value-at-risk for financial variables, evaluating inflation targeting based on the distribution of inflation and inflation volatility, the size effects of volatility spillovers for firm performance and exchange rates in tourism, forecasting volatility with the realized range in the presence of noise and non-trading, using CARRX models to study factors affecting the volatilities of Asian equity markets, deciphering the Libor and Euribor spreads during the subprime crisis, information transmission between sovereign debt CDS and other financial factors for Latin America, time-varying mixture GARCH models and asymmetric volatility, and diagnostic checking for non-stationary ARMA models with an application to financial data.     

Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach

This study discusses the validation of an agent-based model of emergent city systems with heterogeneous agents. To this end, it proposes a simplified version of the original agent-based model and subjects it to mathematical analysis. The proposed model is transformed into an analytically tractable discrete Markov model, and its city size distribution is examined. Its discrete nature allows the Markov model to be used to validate the algorithms of computational agent-based models. We show that the Markov chains lead to a power-law distribution when the ranges of migration options are randomly distributed across the agent population. We also identify sufficient conditions under which the Markov chains produce the Zipf׳s Law, which has never been done within a discrete framework. The conditions under which our simplified model yields the Zipf׳s Law are in agreement with, and thus validate, the configurations of the original heterogeneous agent-based model.     

Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies

How to accurately predict financial distress is an important issue for enterprise managers, investors, creditors and supervisors. In this paper we develop SVM models (Support Vector Machine) and MDA (Multivariate Discriminant Analysis) models, using Chinese listed companies as our sample. The empirical results show that the prediction ability of SVM models outperforms the MDA models. Additionally, internal governance and external market variables, as well as macroeconomic variables are added as the predictive variables. The results indicate that these variables have theoretical and empirical linkage with the financial distress of Chinese listed companies.     

Systemic financial risk indicators and securitised assets: an agent-based framework

The paper presents an agent-based model of a credit economy which includes a securitisation process and a bailout mechanism for bank bankruptcies. Within this framework, banks are able to sell mortgages to a financial vehicle corporation, which finances its activity by creating mortgage-backed securities and selling them to a mutual fund. In turn, the mutual fund collects liquidity by selling shares to households and remunerates them with a monthly interest. The impact of this mechanism is analysed by means of computational experiments for different levels of banks’ securitisation propensity. Furthermore, we study a set of systemic risk indicators which have the aim of assessing the imbalances in the financial system. Two of them are the mortgage-to-GDP ratio and the capital adequacy ratio, which are constructed to detect only the on-balance sheet changes in banks’ credit exposure. We consider two additional indicators, similar to the previous ones with the only difference that they are also able to account for the off-balance sheet items. Moreover, we adopt an indicator, the so-called “virtuous–unvirtuous cycle” indicator, which, besides off-balance assets, targets also the GDP. The results show that higher securitisation propensity weakens the financial stability of banks with relevant effects on different sectors of the economy. Most importantly, the analysis of systemic risk reveals the important issue of designing suitable systemic risk indicators for predicting incoming financial crises, finding that an essential feature of these indicators should be to integrate banks’ off-balance sheet assets.

     

THE FINANCIAL REPRESSION‐LIBERALIZATION DEBATE: TAKING STOCK,LOOKING FOR A SYNTHESIS

Financial repression and liberalization became the object of fierce debates between academics and policy makers since the early 1970s. As of the late 1980s, financial liberalization became also part of the ‘Structural Adjustment Programs’ sponsored by the International Monetary Fund and the World Bank. However, the literature on financial repression and liberalization remains controversial on its theoretical conclusions and policy implications. Given its importance for both the theory and policy of economic development and growth, this paper looks for a possible synthesis. After reviewing the theoretical contributions and empirical studies under the headings of the McKinnon–Shaw and new structuralists models, the survey concludes that a new synthesis might be found in the Post‐Keynesian attempt to take an institutional perspective within a globalised financial and economic environment.     

Business and consumer expectations and macroeconomic forecasts

Business and consumer surveys have become an essential tool for gathering information about different economic variables. While the fast availability of the results and the wide range of variables covered have made them very useful for monitoring the current state of the economy, there is no consensus on their usefulness for forecasting macroeconomic developments.The objective of this paper is to analyse the possibility of improving forecasts for selected macroeconomic variables for the euro area using the information provided by these surveys. After analyzing the potential presence of seasonality and the issue of quantification, we tested whether these indicators provide useful information for improving forecasts of the macroeconomic variables. With this aim, different sets of models have been considered (AR, ARIMA, SETAR, Markov switching regime models and VAR) to obtain forecasts for the selected macroeconomic variables. Then, information from surveys has been considered for forecasting these variables in the context of the following models: autoregressive, VAR, Markov switching regime and leading indicator models. In all cases, the root mean square error (RMSE) has been computed for different forecast horizons.The comparison of the forecasting performance of the two sets of models permits us to conclude that, in most cases, models that include information from the surveys have lower RMSEs than the best model without survey information. However, this reduction is only significant in a limited number of cases. In this sense, the results obtained extend the results of previous research that has included information from business and consumer surveys to explain the behaviour of macroeconomic variables, but are not conclusive about its role.     

Direct comparison of agent-based models of herding in financial markets

The present paper tests a new model comparison methodology by comparing multiple calibrations of three agent-based models of financial markets on the daily returns of 24 stock market indices and exchange rate series. The models chosen for this empirical application are the herding model of Gilli and Winker (2003), its asymmetric version by Alfarano et al. (2005) and the more recent model by Franke and Westerhoff (2011), which all share a common lineage to the herding model introduced by Kirman (1993). In addition, standard ARCH processes are included for each financial series to provide a benchmark for the explanatory power of the models. The methodology provides a consistent and statistically significant ranking of the three models. More importantly, it also reveals that the best performing model, Franke and Westerhoff, is generally not distinguishable from an ARCH-type process, suggesting their explanatory power on the data is similar.