Planning road networks in landslide-prone areas: A case study from the northern forests of Iran

Road development in mountainous forestlands can lead to naturally formed slopes being disturbed. Efficient planning methodologies can assist forest engineers in either preventing or reducing the environmental impacts of forest roads. In this study, we first prepared an inclusive landslide susceptibility map for our study area using a Bayesian model. We next designed 12 road network alternatives using PEGGER, an ArcView GIS extension. We extracted the values of these alternatives from the susceptibility map to evaluate their environmental feasibility. We also evaluated the alternatives from an economic perspective using a model for conducting cost estimates of the forest roads. Our results show that this approach is useful for identifying the alternative that meets environmental and economic goals. The policy implications of this study suggest that if the development of road networks in mountainous areas is an important forestry goal, mechanisms should be aimed primarily at controlling slope instability.     

IMPORTED SKILL‐BIASED TECHNOLOGICAL CHANGE IN DEVELOPING COUNTRIES

This paper discusses the occurrence of skill‐enhancing technology import, namely, the relationship between imports of embodied technology and widening skill‐based employment differentials in low‐ and middle‐income countries. Generalized method of moments (GMM) techniques are applied to an original panel dataset comprising 28 manufacturing sectors for 23 countries over a decade. Econometric results provide robust evidence of the determinants of widening employment differentials in low‐ and middle‐income countries. In particular, the proposed empirical evidence indicates capital–skill complementarity as a possible source of skill bias, while imported skill‐enhancing technology emerges as an additional driver of increasing demand for the skilled workers in these countries.     

Two-step estimation of panel data models with censored endogenous variables and selection bias

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.     

The effects of real exchange rate depreciation on stochastic producer prices in low-income agriculture

This paper considers the effects of real exchange rate depreciation on stochastic agricultural producer prices in low-income agriculture. Conventional wisdom, that real depreciation achieved through nominal currency devaluation stimulates tradables production, does not universally hold in the presence of stochastic prices. In fact, real depreciation is only stimulative in two cases–importables that remain importable and nontradables that become exportable. GARCH estimation of time-series price data on several commodities from Madagascar support the hypotheses generated by the analytical model.     

使用粒子群算法解决期权定价模型参数校准问题——以heston模型为例

期权定价模型的参数校准问题是一个常见的难题,以heston 模型为例,定价时需要估计6 个参数,参数估计问题实质上是高维非线性规划问题,由于估参函数的性质不好,一般的估参方法常常失效。使用粒子群（PSO）智能算法可以改善该模型的参数校准问题,因为粒子群算法具有内在随机性,因此参数估计中的局部极小值问题可以被较好地解决。使用2017 年12 月20 日的香港恒生指数期权作为估计样本,并对2017 年12 月25 日的期权进行样本外预测,数值结果表明使用heston 模型对期权进行定价并配合粒子群算法估计参数具有良好的定价效果。     

Joint estimation using revealed and stated preference data: An application using a national forest

We combine contingent behaviour with travel cost data to estimate the change in the recreational use value of a National Forest due to quality and price changes. Instead of the usual improvement scenario, a hypothetical deterioration in the conditions of the forest due to a fire is considered. A dataset containing five observations for each respondent enabled the estimation of three models for which the number of scenarios differed. The results show that visitors are sensitive to price and quality changes and that in the forest fire scenario the intended number of trips would be reduced and that respondents would experience a welfare loss. Signs of inconsistency between preferences expressed by revealed and intended behaviour were found. This research also provides some indications that strategic bias affects answers to price changes.     

Pension plan solvency and extreme market movements: a regime switching approach

We develop and test a new approach to assess defined benefit (DB) pension plan solvency risk in the presence of extreme market movements. Our method captures both the ‘fat-tailed’ nature of asset returns and their correlation with discount rate changes. We show that the standard assumption of constant discount rates leads to dramatic underestimation of future projections of pension plan solvency risk. Failing to incorporate leptokurtosis into asset returns also leads to downward biased estimates of risk, but this is less pronounced than the time-varying discount rate effect. Further modifying the model to capture the correlation between asset returns and the discount rate provides additional improvements in the projection of future pension plan solvency. This reduces the perceived future risk of underfunding because of the negative correlation between interest rate changes and asset returns. These results have important implications for those with responsibility for balancing risk against expected return when seeking to improve the current poor funding positions of DB pension schemes.     

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution. For a cross section of 18 stock market indices, we notice that (threshold) (T)GARCH-implied model innovations are likely at odds with the commonly held IID assumption. Two complementary strategies are pursued to evaluate the conditional distributions of consecutive TGARCH innovations, a non-parametric approach and a class of standardized copula distributions. Modelling higher order dependence patterns is found to improve standard TGARCH-implied conditional value-at-risk and expected shortfall out-of-sample forecasts that rely on the notion of IID innovations.     

The shape of small sample biases in pricing kernel estimations

Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In the first step, we analyse theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader regarding potential computational issues coupled with statistical techniques. In the second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kernels typically violate the commonly believed monotonicity at the centre even when the true pricing kernel fulfils these. This contributes to an alternative, statistical explanation for the puzzling shape in pricing kernel estimations.