Ángel León

In the valuation of forest resources, the alternative use of the land is one of the central themes. In most cases it is made without taking into account the uncertainty and the possible flexibility of the alternative use. Within these alternatives, the strategy of shifting to a more profitable and sustainable crop is a well-studied topic in forest research. Although the transformation opportunity could add great value to the project, the valuation of this flexibility is obviated by traditional discounted cashflow criteria (NPV). The application of real options theory (ROT) makes it possible to assess this flexibility based on the uncertainty that the transformation entails. However, the hypotheses that are made about the future evolution of the underlying asset, in this case the value of the new crop, may condition the precision of the result. Usually some researchers model these conversions under the hypothesis of geometric Brownian motion (GBM), hypotheses that are not plausible when the new crop has a strong seasonal component. In this work, an adapted model framework is proposed to evaluate forest transformation opportunity into another crop when land use has both high agronomic potential and high seasonal component, a context in which classic real options framework is not applicable. As a work based on a theoretical model, after methodological motivation, the strawberry crop is chosen as alternative due to its seasonal component. Using private data for this crop, we model through the Ornstein–Uhlenbeck process, with mean-reversion (MR) to a seasonal component, and then we use of Longstaff and Schwartz’s algorithm to calculate the option value. The results show that when considering flexibility in option valuation it leads to an increase on the return of more than 4%. Furthermore, robustness analysis evidence shows that option value is very sensitive to seasonal component, reinforcing previous evidence that suggests that the MR process offers a more accurate and appropriate valuation over the traditional GBM in the arena of agronomic potential valuation. Specifically, the result of valuing this transformation through the MR process is between 1.5 and 1.7 times the value of the NPV, which results in approximately a 13% annual return. If GBM had been used, the valuation would have been a 72% annual return, an unrealistic result in this context, due to the non-consideration of the seasonal mean-reverting prices process.

We assess Value-at-Risk (VaR) and Expected Shortfall (ES) estimates assuming different models for the standardized returns: distributions based on polynomial expansions such as Cornish-Fisher and Gram-Charlier, and well-known parametric densities such as normal, skewed-t and Johnson. This paper aims to analyze whether models based on polynomial expansions outperform the parametric ones. We carry out the model performance comparison in two stages: first, with a backtesting analysis of VaR and ES; and second, using loss functions. Our backtesting results show that all distributions, except for normal ones, perform quite well in VaR and ES estimations. Regarding the loss function analysis, we conclude that polynomial expansions (specifically, the Cornish-Fisher one) usually outperform parametric densities in VaR estimation, but the latter (specifically, the Johnson density) slightly outperform the former in ES estimation; however, the gains of using one approach or the other are modest.

We present a polynomial expansion of the standardized Student-t distribution. Our density, obtained through the polynomial adjusted method in Bagnato, Potí, and Zoia (2015. “The Role of Orthogonal Polynomials in Adjusting Hyperbolic Secant and Logistic Distributions to Analyse Financial Asset Returns.” Statistical Papers 56 (4): 1205–12340), is an extension of the Gram–Charlier density in Jondeau and Rockinger (2001. “Gram-Charlier Densities.” Journal of Economic Dynamics and Control 25 (10): 1457–1483). We derive the closed-form expressions of the moments, the distribution function and the skewness–kurtosis frontier for a well-defined density. An empirical application is also implemented for modeling heavy-tailed and skewed distributions for daily asset returns. Both in-sample and backtesting analysis show that this new density can be a good candidate for risk management.

Purpose: This paper aims to focus on non-poor households that during the Great Recession experienced economic stress (ES). That is, whose economic comfort was reduced taking into account their previous living standards. The paper seeks to determine how the crisis has affected this extensive (and key) social group. Design/methodology/approach: The analysis has been performed in a dynamic way. The non-poor households ES situation and its changes are studied throughout the period 2008-2016 by taking the four-year intervals provided by the longitudinal Spanish Living Conditions Survey. The authors discuss and select the circumstances to determine whether ES has occurred. To identify which variables determine the probability of suffering ES the authors use a standard logit model. Findings: The main variable is the tenure status of the dwelling: property with a mortgage or rented multiply the risk of ES by up to 3.5 times. ES falls as the household’s work intensity increases. However, an improvement in the employment situation cannot be associated with a reduction in ES probability. The main socio-demographic variables behave as predicted: woman householder, grow in the number of household members and bad health increase the risk of ES, and the higher the level of education of the householder, the lower the risk. Originality/value: There are very few studies regarding the people above the poverty line. Exploring and analyzing the factors determining the sensitivity of the largest part of the population to the crisis is very relevant, as the pace of the economic recovery depends largely on them.