A market-consistent framework for the fair evaluation of insurance contracts under Solvency II

Decisions in Economics and Finance - The entry into force of the Solvency II regulatory regime is pushing insurance companies in engaging into market consistence evaluation of their balance sheet,...     

Urban Economic Openness and IPO Underpricing

This paper represents a first attempt to employ a macroeconomic approach to explain the high and varying IPO underpricing within a single emerging market. We examine the empirical impact of trade openness on the short-run underpricing of initial public offerings (IPOs) using city-level data. Particularly, we argue that urban economic openness (UEO) has a significant impact on the productivity and on prices of both direct and indirect real estate due to productivity gains of companies in more open areas. This in turn positively affects the firm’s profitability, enhancing the confidence in local real estate markets and future company performance, hence decreasing the uncertainty of the IPO valuation. As a result, issuers have less incentive to underprice IPO shares. We use a sample of Chinese real estate IPOs, which offer a suitable laboratory thanks to their strong geographic investment patterns focused locally and a country with a highly heterogeneous openness across regions. Controlling for traditional firm- and issuing-specific characteristics of IPOs that are used for developed markets and Chinese-related features (i.e. listing location and state ownership), we find the evidence that companies investing in economically more open areas experience less IPO underpricing. Our results show great explanatory power and are robust to different specifications.     

Analytic derivatives and the computation of GARCH estimates

In the context of univariate GARCH models we show how analytic first and second derivatives of the log-likelihood can be successfully employed for estimation purposes. Maximum likelihood GARCH estimation usually relies on the numerical approximation to the log-likelihood derivatives, on the grounds that an exact analytic differentiation is much too burdensome. We argue that this is not the case and that the computational benefit of using the analytic derivatives (first and second) may be substantial. Furthermore, we make a comparison of various gradient algorithms that are used for the maximization of the GARCH Gaussian likelihood. We suggest the implementation of a globally efficient computation algorithm that is obtained by suitably combining the use of the estimated information matrix with that of the exact Hessian during the maximization process. As this would appear a straightforward extension, we then study the finite sample performance of the exact Hessian and its approximations (that is, the estimated information, outer products and misspecification robust matrices) in inference.     

A medium-N approach to macroeconomic forecasting

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting.     

The Power of the Euro–Sterling Rates in Explaining Asset Market Rates: A High‐frequency Analysis

This article makes use of high‐frequency asset market data to explain unexpected changes in interest rates using the methodology proposed by Cochrane and Piazzesi (2002) . This work departs from the existing literature because it uses UK market expectations to capture unexpected movements in the base rate, and explores its effect on a large number of asset market variables. Results indicate that the relation between asset market data and unexpected base rate changes is stronger and more consistent than the relation between asset market data and raw base rate changes. Results appear to be robust to extreme value changes.     

Risk-type density diagrams by crash type on two-lane rural roads

The research presented here aims to plot density diagrams per road crash risk type to identify all possible scenarios where driving is less than safe. The starting point was the prediction of injury crash rate on horizontal homogeneous segments of two-lane rural roads for three main injurious crash types (head-on/side collisions, rear-end crashes, and single-vehicle run-off-road crashes) as observed on the network. A careful analysis of the database shows that a wide variety of factors appear to be influenced or associated with the crash dynamic, as follows: the road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash), mean lane width, the horizontal curvature indicator (measurement of the curvature change rate), and mean speed. Crashes recorded from 2003 to 2010, of which 1597 were injurious, and 645 resulted only in damage to property, were analyzed on more than 3700?km of road network in Southern Italy. Generalized estimating equations with a negative binomial distribution were implemented. Risk-type density charts were plotted to thoroughly identify all possible combinations of existing explicative variables producing hazardous conditions on the road. The different shades in the diagrams represent different ranges of injurious crash rates: the white band shows low levels, while a black band shows high values. It is not possible to consider working on an explanatory variable to reduce hazardous conditions on the road network without also considering how this variation might affect the influence of the remaining explanatory variables on crash phenomena and, consequently, on the predictive model. The risk maps make it possible to keep under control in a simple and immediate approach the way each variable as a result of variations of a part or of all.     

National Health Service Boards of Directors and Governance Models

Abstract

This article reports the findings of a year-long research project focused on the activity of boards of directors of twenty-two trusts from the British National Health Service (NHS). The evidence gathered through the use of semi-structured interviews, focus groups, workshops, feedback questionnaires and document analysis indicates that the behavioural dynamics of boards, affected by the dominance of the expert model, act as antecedents of their statutory functions and the implementation of different governance models. Only a portion of the boards involved has effectively incorporated in its modus operandi post-New Public Management (post-NPM) principles of governance.     

Exploring costs and benefits of compliance with HACCP regulation in the European meat and dairy sectors

A Hazard Analysis and Critical Control Point (HACCP) Program was introduced as a mandatory measure in the EU in the 1990s. Despite its impact on the food industry, only limited literature addresses the issue of cost and benefits of HACCP at the firm level in Europe. This paper illustrates the results of a pilot study on case studies in Italy, UK and The Netherlands, providing a first assessment of the order of magnitude of costs of compliance and a qualitative illustration of the main benefits perceived by producers.     

Credit Risk Models – Do They Deliver Their Promises? A Quantitative Assessment

We develop a framework to assess the statistical significance of expected default frequency calculated by credit risk models. This framework is then used to analyse the quality of two commercially available models that have become popular among practitioners: KMV Credit Monitor and RiskCalc from Moody's.
Using a unique database of expected default probability from both vendors, we study both the consistency of the prediction and its timeliness. We introduce the concept of cumulative accuracy profile (CAP) that allows to see in one curve the percentage of defaulting companies captured by the models one year in advance. We also use the Miller's information test to see if the models add information to the S&P rating.
The result of the analysis indicates that these models indeed add relevant information not accounted for by rating alone. Moreover, with respect to rating agencies, the models predict defaults more than ten months in advance on average.
(J.E.L.: C52).     

Ex ante purchasing power parity: An empirical note

Conclusions In the paper, the EAPPP theory was discussed and three different tests with increasingly statistic power were proposed and implemented. The results are mixed, since the theory passed the randomness and orthogonality tests, but not the direct regression test. The impression is that EAPPP seems to hold in a weak sense, since the evidence confirms random movements of real exchange rates and an efficient use of information by agents but not the constraints on (14) postulated by EAPPP. In general, the results of the present study are in accordance with those of Cumby and Obstfeld [1984], Mishkin [1984], MacDonald [1985] and Gaab et al. [1986] who found only weak support to the EAPPP theory. Furthermore, in the case of the dollar-lira exchange rate, our findings are different from those of Tronzano [1987] according to which “although PPP holds very badly in its traditional (absolute or relative formulation), ex ante PPP (both in “weak” and in “strong” version) [is] highly supported by data”. In fact, in our case both the second orthogonality test and the regression test are unfavourable to the EAPPP.