The evaluation of forest crop damages due to climate change. An application of Dempster–Shafer method

In order to assess damage risk caused by climate change in forest areas, Dempster–Shafer theory of evidence and fuzzy measures were applied to develop a framework for the estimation of economic forest damage. According to the definition of risk supported by the Intergovernmental Panel on Climate Change, a function of hazard and resilience lines of evidence was defined. The results of the hazard and resilience assessment were used to develop an economic framework based on Faustmann studies. The evaluation model, implemented through a spatial analysis procedure, was carried out linking Faustmann formula with hazard and resilience raster maps. The model permitted to estimate in monetary terms two possible costs to be supported: the first one is expressed as the expected damage to the forest crop on the basis of the current obtainable woody assortments and the second one referred to the potential expenses to pay in order to mitigate the risk. Finally, the framework was tested on an area of central Italy (Tuscany region).     

Analysis of ultra-high-frequency financial data using advanced Fourier transforms

This paper presents a novel application of advanced methods from Fourier analysis to the study of ultra-high-frequency financial data. The use of Lomb–Scargle Fourier transform, provides a robust framework to take into account the irregular spacing in time, minimising the computational effort. Likewise, it avoids complex model specifications (e.g. ACD or intensity models) or resorting to traditional methods, such as (linear or cubic) interpolation and regular resampling, which not only cause artifacts in the data and loss of information, but also lead to the generation and use of spurious information.     

Structural comparisons of networks and model-based detection of small-worldness

In this paper, we consider the problem of assessing the “level of small-worldness” of a graph and of detecting small-worldness features in real networks. After discussing the limitations of classical approaches, based on the computation of network indicators, we propose a new procedure, which involves the comparison of network structures at different “observation scales”. This allows small-world features to be caught, even if “hidden” deeply into the network structure. Applications of the procedure to both simulated and real data show the effectiveness of the proposal, also in distinguishing between different small-world models and in detecting emerging small-worldness in dynamical networks.     

Information acquisition,referral, and organization

Each of two experts may provide a service to a client. Experts' cost comparative advantage depends on an unknown state, but an expert may exert effort to get a private signal about it. In a market, an expert may refer the client to the other for a fee. In equilibrium, only one expert exerts effort and refers, and the equilibrium allocation is inefficient. Referral efficiency can be restored when experts form an organization, in which a referring expert must bear the referred expert's cost. However, the referred expert shirks from work effort because of the lack of cost responsibility.     

Selecting structural innovations in DSGE models

Dynamic stochastic general equilibrium (DSGE) models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are “nonexistent” and propose a method to select the economic shocks driving macroeconomic uncertainty. Forcing these nonexisting shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select shocks and estimate model parameters with high precision. We revisit the empirical evidence on an industry standard medium‐scale DSGE model and find that government and price markup shocks are innovations that do not generate statistically significant dynamics.     

Interlocking directorates in Italy: persistent links in network dynamics

In this work we study the time evolution of interlocking directorates in Italy from 1998 to 2011 by means of dynamical networks. Our purpose is to assess if in Italy there is a connected and stable structure, due to the presence of directors with multiple mandates, like it happens in the German case. We find a very cohesive network structure, due to the presence of a few directors with multiple assignments and, unlike the German case, this structure is stable, but not connected. Moreover we propose an alternative approach to investigate the dynamics, based on temporal networks, in order to quantify the variation of links in a certain time period. We construct a unique cumulative network, where nodes are companies and the existence of an edge is related with the persistence in time of an interlock between two companies. This persistence is due on the one hand to the ownership of a few family firms, and on the other hand to cross-shareholdings between companies. To complete the analysis we also investigate whether the link stability results from the appointments of the same director or from the stepping in/out of different directors.     

Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis

This article investigates the behaviour of the European banking system during the financial crises that occurred in the last decades. Among the various approaches for measuring systemic risk, we consider network analysis, which describes the linkages among financial institutions and their whole structure. We construct a time-varying network of the European banking system. Banks are linked to form a global interconnected system and they mutually influence one another in terms of risk. We model their reciprocal influence via a weighted and directed network, in which weights are related to risk measures that are based on equity returns. Then, we apply two network indicators to investigate the prominence of a bank in spreading and receiving risk from the others. The results enable us to capture many features of the banking system while identifying the global systemically important banks. Moreover, the results of the analysis over time show how interconnections change over periods that are characterized by various economic scenarios.

     

Impact is not just volatility

With a simple scaling argument we show empirically that impact growing as the square-root of trading volume has nothing to do with diffusion price changes growing as the square root of time     

Supply chain finance: From traditional to supply chain credit rating

Traditional credit rating models, adopted by financial institutions to assess the credit risk of a company, adopt a purely financial perspective, and often fail to properly assess small and medium enterprises. On the other hand, buyers usually assess suppliers by means of comprehensive vendor ratings, considering a broad range of operational performance. This paper investigates whether financial and vendor ratings can be integrated into a supply chain credit rating model that jointly considers financial indicators of the supplier and its operational evaluation provided by buyers; the paper also investigates the benefits and the challenges of such a model for all the stakeholders involved (buyers, suppliers, financial institutions, and technology providers), adopting the lenses of the stakeholder theory. We adopted both multiple case studies and an iterative focus group, involving representatives from suppliers, buyers, financial institutions, and technology providers. The results confirm the potential value of such an integrated rating, mainly for strategic suppliers, showing the expected benefits for all stakeholders and highlighting the potential challenges to face.     

Forecasting With the Standardized Self‐Perturbed Kalman Filter

We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun (Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the amount of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other online algorithms and to classical and Bayesian methods. The standardized self‐perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determines the extent to which realized variance can be used to predict excess returns. Copyright © 2016 John Wiley & Sons, Ltd.