The information compliance indexes. The illustrative case of income taxes

The adoption of IASB’s standards has represented, in the European Union, an important effort of harmonization towards the financial reporting comprehensiveness, reliability, relevance and comparability. This paper seeks to highlight the importance of Information Compliance Indexes (ICI), based on the accounting standards, as a proxy for reporting quality awareness. This approach is evidenced through an illustrative example about disclosures on deferred taxes, as required by IAS 12. This standard prescribes the accounting treatment for current taxes, deferred assets and liabilities. These issues are usually perceived by stakeholders as indicators of companies’ continuity and potential future returns. Based on non-financial listed companies of Euronext Lisbon regulated market, with reference to the end of fiscal years 2008 and 2012, an information compliance index was performed, based on that accounting standard. Then, this index was regressed with a set of performance and control indicators. Evidences have provided several statistical significant insights, which corroborate the findings that information compliance and disclosure levels depend from several performance and control indicators.     

Classification of micro,small and medium enterprises (M-SME) based on their available levels of knowledge

This paper proposes a novel and practical method of classifying micro, small and medium enterprises (M-SME) based on their available levels of knowledge. This classification considers that the specific problems that dominate a company’s operations are related to the level of knowledge available. Therefore, identifying the dominant problem groups facing the company can enable an estimate of the level of knowledge available in the enterprise.Data from 2698 Mexican M-SMEs are used to identify operational problems. The main problem groups are obtained through a cluster analysis. The proposed classification consists of three levels of knowledge based on the interpretation of the dominant problem groups in each enterprise. Each of the 2698 Mexican M-SME are classified by a discriminant analysis.Nearly half of the M-SME are classified as having a lower level of available knowledge, and only 10% are classified with the highest level. No difference is observed between the size of the company and the level of available knowledge. This means that growth in the number of employees and sales of M-SMEs is not necessarily accompanied by an accumulation of knowledge that companies can use to improve their operations.     

Comparing efficiency of holding business model and individual management model of airports

This research compares the efficiency of holding business model to individual management model of airports, employing some robust non-parametric partial frontier-based methods to compare the statistical distributions of efficiency, under different scenarios, to find out which group of airports yields better global performance. The comparison between groups will follow a Malmquist index decomposition, which seems to be the most appropriate tool for within- and inter-group performance comparison. For this purpose, a sample of 145 airports from three continents is utilized. The results provide evidence that European airports are the most productive ones, and within this cluster, the individual management model presented a significant frontier shift with respect the holding cluster frontier, meaning that the former is much more productive than the latter.     

Efficiency and productivity changes in Greek airports during the crisis years 2010–2014

The aim of this study is to evaluate the operating efficiency and productivity changes of the Greek airports, during the first years of the severe economic crisis in Greece (2010–2014), by using two methods: Data Envelopment Analysis (DEA) and Malmquist Productivity index (MPI). Findings have shown that, despite the dramatic effects of the economic crisis on the socio-economic life of the country, overall airport efficiency and productivity improved, mainly due to exogenous factors such as international tourism growth. The MPI reveals that over the period of the study, airports have experienced an annual average increase in total factor productivity (TFP) of 0.9% (an increase of 3.6% over the examined period). On examining the components of this productivity change, it becomes evident that this is due to the combination of both positive (a slight progress) annual average technology change (0.5%) and technical efficiency change (0.4%). The results also indicate that 65.8% of airports have an increase in average TFP during the period 2010–2014, ranging between 0.4% and 20%. However, as Greek airports operate at poor levels of efficiency, there is still considerable space for improvements in most of the airports.     

Relationship between oil,stock prices and exchange rates: A vine copula based GARCH method

In this paper, we apply a vine copula approach to investigate the dynamic relationship between energy, stock and currency markets. Dependence modeling using vine copulas offers a greater flexibility and permits the modeling of complex dependency patterns for high-dimensional distributions. Using a sample of more than 10 years of daily return observations of the WTI crude oil, the Dow Jones Industrial average stock index and the trade weighted US dollar index returns, we find evidence of a significant and symmetric relationship between these variables. Considering different sample periods show that the dynamic of the relationship between returns is not constant over time. Our results indicate also that the dependence structure is highly affected by the financial crisis and Great Recession, over 2007–2009. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches.     

A NEW LOOK AT SHORT‐TERM IMPLIED VOLATILITY IN ASSET PRICE MODELS WITH JUMPS

We analyze the behavior of the implied volatility smile for options close to expiry in the exponential Lévy class of asset price models with jumps. We introduce a new renormalization of the strike variable with the property that the implied volatility converges to a nonconstant limiting shape, which is a function of both the diffusion component of the process and the jump activity (Blumenthal–Getoor) index of the jump component. Our limiting implied volatility formula relates the jump activity of the underlying asset price process to the short‐end of the implied volatility surface and sheds new light on the difference between finite and infinite variation jumps from the viewpoint of option prices: in the latter, the wings of the limiting smile are determined by the jump activity indices of the positive and negative jumps, whereas in the former, the wings have a constant model‐independent slope. This result gives a theoretical justification for the preference of the infinite variation Lévy models over the finite variation ones in the calibration based on short‐maturity option prices.     

Kernel-type estimators for the distortion risk premiums of heavy-tailed distributions

A new kernel-type estimator for the distortion risk premiums of heavy-tailed losses is introduced. Using a least-squares approach, a bias-reduced version of this estimator is proposed. Under suitable assumptions, the asymptotic normality of the given estimators is established. A small simulation study, to illustrate the performance of our method, is carried out.     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Measuring systemic risk with regime switching in tails

This paper motivates the importance of modeling nonlinearities in measuring systemic risk. I capitalize this motivation by generalizing the CoVaR approach proposed by Adrian and Brunnermeier (2016) to allow it switching between a high and a normal risk regime filtered from data.. Considering the U.S. large bank holding companies (BHCs), this paper shows that modeling regime changes in tails is capable of capturing both amplification and mean-reversion effects of an adverse shock to a bank's balance sheet on the banking system. Using the Kolmogorov–Smirnov test statistics with and without bootstrapping, I perform the significance test to identify systemically important financial institutions (SIFIs), and the stochastic dominance test to rank the identified SIFIs. The stochastic dominance test raises the concern that the CoVaR measure underestimates systemic risk contributions for SIFIs but overestimates for non-SIFIs. Finally, applying the BHCs' characteristics and housing market price to forecast the regime-switching systemic risk out-of-sample, I obtain from 4- and 8-quarter-ahead horizons a desirable countercyclical, forward-looking measure of systemic risk.     

有关过程能力指数基准的若干问题

基准是过程能力指数的一个重要属性，对有关过程能力指数基准的若干问题作出详尽阐述，可为进一步纠正过程能力指数公式错误（诸如Cpk、CpU、CpL等）提供了理论基础。