Christophe Hurlin

A Theoretical and Empirical Comparison of Systemic Risk Measures

We derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g., beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk.

Testing Convergence : A Panel Data Approach

This paper briefly presents new empirical tests of the convergence hypothesis based on panel data. We apply a modified Evans and Karras [1996] testing procedure to three samples (Europe, OECD and World). We propose a nested tests procedure to characterize various convergence processes: absolute or conditional, with or without a common speed of convergence. In the conditional convergence context, we are particularly concerned with the information included in individual effects. We find evidence supporting the existence of an absolute and common convergence process for per capita GDP in the European Union sample (1960-1990). As concerns the OECD sample, structural disparities imply only a conditional convergence process occurring at country-specific speeds. For our World sample (86 countries) there is no convergence process at all. For OECD countries, a first study of fixed effects is proposed. It shows that only the private and public investment ratios are significantly linked to those unobservable structural disparities.

What would Nelson and Plosser find had they used panel unit root tests

In this study, we systemically apply nine recent panel unit root tests to the same 14 macroeconomic and financial series as those considered in the seminal paper by Nelson and Plosser (1982). The data covers OECD countries from 1950 to 2003. Our results clearly point out the difficulty that applied econometricians would face when they want to get a simple and clear-cut diagnosis with panel unit root tests. We confirm the fact that panel methods must be very carefully used for testing unit roots in macroeconomic or financial panels. More precisely, we find mitigated results under the cross-sectional independence assumption, since the unit root hypothesis is rejected for many macroeconomic variables. When international cross-correlations are taken into account, conclusions depend on the specification of these cross-sectional dependencies. Two groups of tests can be distinguished. The first group tests are based on a dynamic factor structure or an error component model. In this case, the nonstationarity of common factors (international business cycles or growth trends) is not rejected, but the results are less clear with respect to idiosyncratic components. The second group tests are based on more general specifications. Their results are globally more favourable to the unit root assumption.

Network Effects of the Productivity of Infrastructure in Developing Countries

Using panel data models, the author examines the threshold effects of the productivity of infrastructure investment in developing countries. He considers various specifications of an augmented production function that allow for endogenous thresholds. More precisely, these specifications are tested in a panel threshold regression model. The authors main robust result is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as noninfrastructure investment. On the contrary, when a minimum network is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investment. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.

Estimates of Government Net Capital Stocks for 26 Developing Countries, 1970-2002

The authors provide various estimates of the government net capital stocks for a panel of 26 developing countries over the period 1970-2001. Two kinds of internationally comparable series of public capital stocks are presented. The first estimates are based on the standard perpetual inventory method and various assumptions regarding initial stocks and depreciation rates. The second set of estimates takes into account the potential inefficiency of public investments in creating capital with a nonparametric approach. Three estimates of net capital stocks are provided, on the basis of three assumptions regarding the efficiency of public investment.

Backtesting value-at-risk accuracy: a simple new test

This paper proposes a new test of value-at-risk (VAR) validation. Our test exploits the idea that the sequence of VAR violations (hit function) ‐ taking value 1 ‐ α, if there is a violation, and ‐α otherwise ‐ for a nominal coverage rate α verifies the properties of a martingale difference if the model used to quantify risk is adequate (Berkowitz et al., 2005). More precisely, we use the multivariate portmanteau statistic of Li and McLeod (1981) ‐ extension to the multivariate framework of the test of Box and Pierce (1970) ‐ to jointly test the absence of autocorrelation in the vector of hit sequences for various coverage rates considered as relevant for the management of extreme risks. We show that this shift to a multivariate dimension appreciably improves the power properties of the VAR validation test for reasonable sample sizes.

RunMyCode.org: A novel dissemination and collaboration platform for executing published computational results

We believe computational science as practiced today suffers from a growing credibility gap - it is impossible to replicate most of the computational results presented at conferences or published in papers today. We argue that this crisis can be addressed by the open availability of the code and data that generated the results, in other words practicing reproducible computational science. In this paper we present a new computational infrastructure called RunMyCode.org that is designed to support published articles by providing a dissemination platform for the code and data that generated the their results. Published articles are given a companion webpage on the RunMyCode.org website from which a visitor can both download the associated code and data, and execute the code in the cloud directly through the RunMyCode.org website. This permits results to be verified through the companion webpage or on a users local system. RunMyCode.org also permits a user to upload their own data to the companion webpage to check the code by running it on novel datasets. Through the creation of “coder pages” for each contributor to RunMyCode.org, we seek to facilitate social network-like interaction. Descriptive information appears on each coder page, including demographic data and other companion pages to which they made contributions. In this paper we motivate the rationale and functionality of RunMyCode.org and outline a vision of its future.

Energy demand models: a threshold panel specification of the ‘Kuznets curve’

This article proposes an original panel specification of the energy demand model. Based on panel threshold regression models, we derive country-specific and time-specific energy elasticity. We find a fall of the elasticity when the income level increase.

Economic Development and Energy Intensity: a Panel Data Analysis

The energy-GDP ratio, or ratio of total national primary energy consumption to GDP, is a measure of the Energy Intensity of the economy (henceforward noted as EI). It represents the energy required to generate a unit of national output. Its evolution over time shows whether the economy becomes more or less energy intensive. Projections of national energy demand under different growth scenarios depend upon the explicit or implicit value of this ratio. It can also be used to define an objective of energy policy.

Network Effects and Infrastructure Productivity in Developing Countries

This study proposes to investigate the threshold effects in the productivity of infrastructure investment in developing countries. It concludes to their presence in the relationship between output and private and public inputs as well as network effects in the productivity of infrastructure. When the available stock of infrastructure is low, investment has the same productivity as non-infrastructure investment. On the contrary, when a minimum network is available, the marginal productivity of infrastructure investment is greater than the productivity of other investments. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.