Narrow Results

This study complements the emerging literature on the COVID-19 pandemic and provides direction, in the case of Nigeria, for targeting monetary policy response to mitigate the pandemic’s economic consequences. We simulate three scenarios: (i) do-nothing; (ii) reduce MPR gradually and (iii) reduce MPR drastically; amidst falling oil prices. The do-nothing scenario, although inflationary, would produce a marginal appreciation of the Naira/USD exchange rate. Gradual or drastic reduction of MPR would deliver relative price stability, but will undermine exchange rate stability and deplete external reserves. MPR should optimally not be reduced below 12% in response to the economic effect of the pandemic.

For a finite planning horizon, there has been a considerable body of research papers in the area of operations management that dealt with four different inventory shortage models in the last two decades. In this paper, we establish the models to reflect the fact that the longer the waiting time, the smaller the backlogging rate. We then use maximizing profit as the objective to make an appropriate comparison among those four alternatives. The theoretical results reveal that Model 4 provides the highest profit only if the profit margin is sufficiently low. Otherwise, in general, Model 3 has the highest profit among them.

We discuss the harvesting of a single forest block from an operations scheduling viewpoint. We report on a harvesting case study, based on practical data from a commercial enterprise, involving minimum and maximum time lags and resource constraints. It appears that the scheduling of the harvesting forest blocks is a significantly different scenario from those represented by the scheduling models available in the literature. The differences come about because: the duration of each operation is dependent upon the combination of constrained resources allocated to it, individual worker-equipment allocation is restricted, and minimum or maximum time lags can be imposed. We report on harvesting operations, a scheduling model, and solution procedures, designed specifically for the case study.

We present a deterministic model that specifies lane direction in a multi-laned bridge that has a movable barrier that divides the two directions of traffic flow, in order to reduce congestion. A probabilistic dynamic programming formulation for a stochastic extension of the model is also presented. Analysis of the special structure of the dynamic programming formulation provides new insights into important aspects of certain traffic planning problems and represents a useful addition to the traffic network planner's toolkit. A case study involving the lane direction management of an actual bridge is also provided.

A series of stock prices typically shows a large trend and smaller fluctuations. These two parts are often studied together, as if parts of a single process; but they appear to be separately caused. In this paper, the two parts are analyzed separately, so that one does not distort the other, and some spurious interaction terms are avoided. This contributes a model, in which a wide range of features of stock price behavior are identified. With logarithms of stock prices, the two parts become of more comparable size. This is found to lead to a simpler additive model. On a logarithmic scale, the stock prices show the trend as a straight line (which can be extrapolated), with added fluctuations filling a narrow band. The trend and fluctuations are thus separated. The trend appears to be largely generated by a positive feedback process, describing investor behavior. The width of the fluctuation band does not grow with time, so positive feedback is not its cause. The movement of stock prices can be understood by analyzing the trend and fluctuations as separate processes; the latter considered as a stationary stochastic process with a scale factor. This analysis is applied to a historical dataset $(S\&P500$ index of daily prices from February 1928). Here, the fluctuations are autocorrelated over short time intervals; there is little structure, except for market crash periods, when variability increases. The slope of the trend showed some jumps, not predictable from price history. This approach to modeling describes many aspects of stock price behavior, which are usually discussed in behavioral finance.

To align with the global goal of keeping temperature below $2^{\circ }$C, a market-based initiative, “Emissions Trading System” (ETS), has been developed to mitigate climate change. However, while the carbon allowances traded at the ETS are mostly held and traded by polluting companies, financial actors engage in “speculation”, activities that might be detrimental to the functioning of the ETS have also invested in the ETS. By drawing from the big data archive of Google Trends, we construct a news-based speculation index to proxy for the role of speculation in the dynamics of carbon pricing. Given our preliminary finding of inherent volatility and the mixed-frequency nature of the dataset, we employ the GARCH-MIDAS econometric technique to test the hypothesis that an all-inclusive framework that reflects the emission compliance and emissions non-compliance dynamics of the ETS is the most accurate approach to modeling carbon prices. We show that higher speculation in the ETS fosters higher long-term volatility in carbon prices, that speculation is a good predictor of carbon prices, and that its positive impact on carbon price returns makes the ETS an attractive investment opportunity. We provide a data-driven framework upon which the growing debate about whether the behavior of the non-compliance emission actors in the ETS endangers or benefits the functioning of the ETS can be evaluated empirically.