Policies for managing peak stock of food grains for effective distribution: A case of the Indian food program

The Indian food program has encountered a significant shortfall in storage due to slow expansion of storage facilities in comparison with procurement. The open storage of food grains results in substantial loss and deterioration of quality. While increasing storage capacity is a viable but costly and time consuming option, the adoption of policies for peak storage reduction would go a long way towards effective food grains management. On this background, this study proposes policy adoption for peak storage reduction for effective inventory management. A dynamic simulation model was built by replicating the complex flow process and incorporating the process variability for finding the bottleneck and significant factors. It was found that steep wheat procurement is the critical bottleneck factor for peak storage requirements. Two practical and straightforward, yet effective policies are proposed from the few existing strategies for peak storage reduction owing to the constraints associated with the food program. With the actual data of the food program, reduction in peak stock was estimated for the recommended policies, including the operational cost saving in storage. The practical implications of these policies within the system were also discussed. Through peak reduction, the use of open storage can be significantly reduced, and this leads to better food grains management for effective food distribution.     

Modeling node heterogeneity in latent space models for multidimensional networks

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This article introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.     

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.     

Are betting returns a useful measure of accuracy in (sports) forecasting?

In an economic context, forecasting models are judged in terms not only of accuracy, but also of profitability. The present paper analyses the counterintuitive relationship between accuracy and profitability in probabilistic (sports) forecasts in relation to betting markets. By making use of theoretical considerations, a simulation model, and real-world datasets from three different sports, we demonstrate the possibility of systematically or randomly generating positive betting returns in the absence of a superior model accuracy. The results have methodological implications for sports forecasting and other domains related to betting markets. Betting returns should not be treated as a valid measure of model accuracy, even though they can be regarded as an adequate measure of profitability. Hence, an improved predictive performance might be achieved by carefully considering the roles of both accuracy and profitability when designing models, or, more specifically, when assessing the in-sample fit of data and evaluating out-of-sample forecasting performances.     

MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES

We examine the performances of several popular Lévy jump models and some of the most sophisticated affine jump‐diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Lévy jump models using stock and option prices. We show that models with infinite‐activity Lévy jumps in returns significantly outperform affine jump‐diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk‐neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.     

A methodology for the choice of the best fitting continuous-time stochastic models of crude oil price

The crude oil price is generally considered as the fundamental factor in the valuation of undeveloped reserves but it is not the unique one. Undeveloped field value also depends on the uncertainty relating to the convenience yield and the risk-free interest rate. The purpose of this paper is to decide on the best continuous-time stochastic models for these risk factors. The Generalized Method of Moments and the Maximum Likelihood Estimation are implemented to fit the parameters of continuous-time stochastic processes. The results of unit root tests without breaks reveal a mean reversion in convenience yield series. Multiple structural change tests show that the risk-free interest rate can be considered constant. The simulation of continuous-time stochastic processes and the mean error between the simulated prices and the market ones show that the Geometric Brownian Motion with jumps is the best model for the oil price compared to the other commonly used processes.     

Forest transition in Northern Spain: Local responses on large-scale programmes of field-afforestation

Public field-afforestation schemes have been designed and widely implemented as a measure to alleviate uneven distribution, generate economic growth in rural areas and maintain or improve natural resources. The literature on forest management and planning has improved land-related information systems, allowing policy-makers to design and implement future policies on the allocation of forestland uses, and to forecast the land requirements of the target population more closely. The aim of this article is to empirically examine and validate the temporal and spatial land use changes and the socioeconomic effects linked to field-afforestation on private lands in the province of Lugo (Galicia, Northern Spain) at the municipal, parish and individual farm holding levels. Two main top-down field-afforestation programmes are reviewed in the study area: the national programme of public afforestations started with the implementation of Ley de Patrimonio Forestal del Estado (National Forest Estate Act; NFE) from 1941 to 2000, and the European aid scheme for forestry measures established under European Council Regulation No. 2080/1992 from 1993 to 1997. The results of a survey, targeted at 1355 farmers and conducted through questionnaires in the province of Lugo in 2004, complete the analysis of public intervention in forest management. The results indicate that the specific social, economic and environmental context of an agricultural and forestry area involves a differential heritage of land customs that determines a differential response model to forest programmes of land allocation and planning. Therefore, this must not be underestimated by the quantitative or qualitative general objectives of large-scale policies.     

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model, which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.     

On Jumps and ARCH Effects in Natural Resource Prices: An Application to Pacific Northwest Stumpage Prices

Continuous-time models of natural resource prices usually preclude the possibility of large changes (jumps) resulting from unexpected events. To test for the presence of jumps and/or ARCH effects, we combine bounds and the Monte Carlo test technique to obtain finite-sample, level-exact p -values. We apply this methodology to stumpage prices from the Pacific Northwest and find evidence of jumps and ARCH effects. To assess the impact of neglecting jumps on the decision to harvest old-growth timber, we develop an autonomous, infinite-horizon stopping model for which we provide a new method of resolution. Our numerical results show the importance of modeling jumps explicitly.     

Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions

Traditionally, logistics design is driven by a need to reduce costs and improve customer service. Recently, the environmental concerns from transport have been increasingly discussed. The traffic levels and associated energy consumption are influenced by supply chain structure, modal split and vehicle utilization. This paper aims to assess the impact of the traditional cost optimization approach to strategic modelling on overall logistics costs and CO₂ emissions by taking into account the supply chain structure (number of depots) and different freight vehicle utilization ratios (90%, 75% and 60%). The simulation model, based on a European case study from the automotive industry, considers strategic and operational level decisions simultaneously. The analysis shows that the optimum design based on costs does not necessary equate to an optimum solution for CO₂ emissions, therefore there is a need to address economical and environmental objectives explicitly as part of the logistics design.