Search Results (41)

This study distinguishes between observed, uncertain, and stochastic uncertain firm growth. Observed firm growth is measured via historical growth of fixed assets scaled by growth of sales revenue. Uncertain firm growth is the volatility of unobserved (estimated error terms) firm growth. The latter is simulated using nonuniform Monte Carlo to generate stochastic uncertain firm growth. The objective of this study is to examine the relationships among the firm specific, economic, and institutional factors that affect the uncertain and stochastic uncertain growth of a firm. The sample includes the nonfinancial firms listed in the DJIA30 and NASDAQ100, covering quarterly data from 1996Q1 to 2022Q4 for 121 companies. The results reveal that (a) sales growth, profitability, cash flow, and long-term financing help reduce a firm’s uncertain growth, (b) high involvement in exporting exposes firms to higher geopolitical uncertainty, (c) institutional quality (especially political stability and regulatory quality) paradoxically contribute to uncertain firm growth. This study contributes to related studies via offering perspectives to firm managers and policy makers about the factors that help manage the uncertainties of firm growth. Full article

This paper aims to develop optional semimartingale methods in risk theory to allow for a larger class of risk models. Optional semimartingales are left-continuous with right-limit stochastic processes defined on a probability space where the usual conditions—completeness and right-continuity of the filtration—are not assumed. Three risk models are formulated, accounting for inflation, interest rates, and claim occurrences. The first model extends the martingale approach to calculate ruin probabilities, the second employs the Gerber–Shiu function to evaluate the expected discounted penalty from financial oscillations or jumps, and the third introduces a Gaussian risk model using counting processes to capture premium and claim cash flow jumps in insurance companies. Full article

This paper is dedicated to studying the optimal investment proportions of three types of assets with symmetry, namely, risky assets, risk-free assets, and wealth management products, when the stochastic expenditure process follows a jump-diffusion model. The stochastic expenditure process is treated as an exogenous cash flow and is assumed to follow a stochastic differential process with jumps. Under the Cox–Ingersoll–Ross interest rate term structure, it is presumed that the prices of multiple risky assets evolve according to a multi-dimensional geometric Brownian motion. By employing stochastic control theory, the Hamilton–Jacobi–Bellman (HJB) equation for the household portfolio problem is formulated. Considering various risk-preference functions, particularly the Hyperbolic Absolute Risk Aversion (HARA) function, and given the algebraic form of the objective function through the terminal-value maximization condition, an explicit solution for the optimal investment strategy is derived. The findings indicate that when household investment behavior is characterized by random expenditures and symmetry, as the risk-free interest rate rises, the optimal proportion of investment in wealth-management products also increases, whereas the proportion of investment in risky assets continually declines. As the expected future expenditure increases, households will decrease their acquisition of risky assets, and the proportion of risky-asset purchases is sensitive to changes in the expectation of unexpected expenditures. Full article

With the implementation of “carbon peaking and carbon neutrality” in China, new energy enterprises, as the vanguard in this strategy, have entered a new era of innovation-driven development. However, enterprises at different lifecycle stages will face different internal and external conditions, and there are differences in their internal mechanisms and business performance. In this case, whether technological innovation efficiency can have an obviously positive effect on their growth and what different effects it can have for enterprises at different lifecycle stages have become issues of great concern to company management, investors, governments, and other stakeholders. This research takes 81 new Chinese energy enterprises as the research objects. First, they are divided into growing, mature, and declining enterprises based on the cash flow combination method. Then, their technological innovation efficiencies from 2016 to 2021 are calculated based on the stochastic frontier method and their growth evaluations are performed by taking both financial and non-financial indicators into consideration. Finally, by taking mediating effects into consideration, the heterogeneity effects of technological innovation efficiency on their growth are studied from the perspective of enterprise lifecycles based on the fixed-effect model. The research results indicate that the technological innovation efficiency of new Chinese energy enterprises has fluctuated around 0.90 in recent years, and is generally at a high level. The efficiency ranking of enterprises at different lifecycle stages is mature period > growing period > declining period. Technological innovation efficiency has a positive impact on their growth, and market share plays a mediating role in this process. The effects of technological innovation efficiency on enterprises at different stages are different, with growing and mature enterprises showing a positive impact. Growing enterprises are more affected by technological innovation efficiency due to their demand for innovation-driven development, while declining enterprises often face difficulties such as unstable operating conditions and outdated equipment, and unreasonable technological innovations may actually accelerate their decline. Full article

The use of renewable energy sources has become strategic in the production of electricity worldwide due to global efforts to increase energy efficiency and achieve a net zero carbon footprint. Hybrid systems can maximize stability and reduce costs by combining multiple energy sources. A conventional metric, such as the levelized cost of energy (LCOE), that is appropriate for assessing the cost-effectiveness of an option may not be appropriate when evaluating the economic feasibility of hybrid systems. This study proposes a stochastic discounted cash flow model (DCF) to assess the economic viability of a hybrid renewable energy system (HRES) in Brazil. The objective is to determine the combinations that will provide the highest 50th percentile internal rate of return (IRR) and the lowest coefficient of variation (CV). Model variables include capital expenditures (CAPEX), operation and maintenance (O&M) costs, sectoral charges, taxes, and long-term energy production metrics. The results demonstrate that the synergies modeled contributed to the higher economic outcomes for the HRES obtained by combining both energy sources rather than opting for a stand-alone configuration. A wind-dominant combination of 60% wind was able to increase the 50th percentile of the IRR, while a solar-dominant combination of 65% solar minimized the CV. Full article

We propose a model for risk adjustment, in the context of IFRS 17, for surrender risk. Surrender rates are assumed to follow a stochastic process, underpinned by data. The distribution of the present value of future individual cash flows is calculated. Using well-known techniques from the theory of convex ordering of stochastic variables, we present closed formula approximations of risk measures, such as quantiles, for the total portfolio. These formulas are easy to program and enable an insurance company to calculate its risk adjustment without time-consuming simulations. Full article

In this article, we focus on considering different risk factors influencing the cash flows of a group of companies. A methodology is suggested for approximated consideration of both seasonal and random fluctuations in the environment, which have some impact on the overall group activity and may be considered via modification of the risk-adjusted discount rates. The main steps of the suggested methodology are described, and the elements of the risk-adjusted discount rate are presented. Although it is the general convention to use the market rate as the discount rate in most cases, under certain circumstances—i.e., stochastic shocks related to the level of interest rates, shifts, and turnabouts in the social environment, as well as the market transformations due to annual/seasonal epidemics, the use of a risk-adjusted discount rate becomes essential. The influence of the seasonal and random changes in the general environment on the companies’ activity through modification of the discount rate is illustrated both numerically and graphically in the article, providing analysis of the impact of exogenous parameters on companies’ output, profits, net present value, and discounted payback period for the initial investment. Full article

Economic feasibility analysis is essential in the decision-making process regarding investment in photovoltaic projects. Project profitability must be measured based not only on the costs and revenues, but also on the climatic particularities of the different locations. Therefore, performing simulations of technical and economic performance of photovoltaic models is fundamental. Thus, the objective of this study was to analyze deterministic and stochastic models of investments in two types of photovoltaic systems, one incorporated into the enterprise’s architecture (a BIPV system) and the other, a conventional one, in different Brazilian locations, covering the predominant climatic factors in the country. The methodological proposal consisted of choosing a city in Brazil with each predominant climate type and compiling its data on irradiation, monthly sunshine hours, and tariffs of the electric power concessionaire, to simulate the electrical generation performance of the proposed photovoltaic systems and their profitability. For the economic analysis, the cumulative probability of positive Net Present Value (NPV) returns was obtained through deterministic simulations in all municipalities. Only the municipality of Pau dos Ferros-RN was chosen to perform 10,000 stochastic simulations, and its cumulative probabilities of positive NPV returns were obtained. In both models of photovoltaic technology analyzed and simulation logics, 100% of the NPVs were positive, indicating profitable cash flows in all scenarios. However, some municipalities obtained better results than others when the climate types favored sunny weather. Moreover, although all cases returned positive NPVs, the conventional model proved to be more economically attractive than BIPV system. Full article

The scheduling of open-pit mine production is a large-scale, mixed-integer linear programming problem that is computationally expensive. The purpose of this study is to create a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. To demonstrate the effectiveness of the proposed research, a case study of an Indian iron ore mine is presented. Multiple realizations of the resource models were developed and integrated within the stochastic production scheduling framework to capture uncertainty and incorporate it into the mine plan. In this case study, two hybrid methods were developed to evaluate their performance. Model 1 is a combined branch and cut with the longest path, whereas Model 2 is a sequential parametric maximum flow and branch and cut. The results show that both methods produce similar materials, ore, metal, and risk profiles; however, Model 2 generates slightly more (4 percent) discounted cash flow from this study mine than Model 1. The results also show that Model 2’s computational time is 46.64 percent less than that of Model 1. Full article

In this paper, we firstly derive the optimal strategies, including greening technology investment, production volume and order quantity decisions with stochastic demand, for the emissions-dependent supply chain composed of one manufacturer and one retailer. Then, an advance purchase discount (APD) contract and an option contract are applied to coordinate the supply chain. Moreover, an innovative prepayment-based option (PBO) contract is designed based on an APD contract and an option contract. We discuss the cash flow, the inventory risk allocation and the impacts of carbon emission tax under each contract. It is found that considering improving cash flow, preselling (or option selling) as a means of supporting the manufacturer with sufficient cash flow will help expand production and invest in greening technology. From the perspective of avoiding inventory risk, the APD contract benefits the manufacturer while the option contract benefits the retailer. However, the PBO contract generates intermediate allocations of inventory risk between manufacturer and retailer. Full article

Proper inventory management is vital to achieving sustainability within a supply chain and is also related to a company’s cash flow through the funds represented by the inventory. Therefore, it is necessary to balance excess inventory and insufficient inventory. However, this can be difficult to achieve in the presence of stochastic demand because decisions must be made in an uncertain environment and the inventory policy bears risks associated with each decision. This study reports an extension of the single-period model for the inventory problem under uncertain demand. We proposed incorporating a Gaussian stochastic process into the model using the associated posterior distribution of the Gaussian process as a distribution for the demand. This enables the modeling of data from historical inventory demand using the Gaussian process theory, which adapts well to small datasets and provides measurements of the risks associated with the predictions made. Thus, unlike other works that assume that demand follows an autoregressive or Brownian motion model, among others, our approach enables adaptability to different complex forms of demand trends over time. We offer several numerical examples that explore aspects of the proposed approach and compare our results with those achieved using other state-of-the-art methods. Full article

Choosing the ideal number of rotations of planted forests under a silvicultural management regime results in uncertainties in the cash flows of forest investment projects. We verified if there is parity in the Eucalyptus wood price modeling through fractional Brownian motion and geometric Brownian motion to incorporate managerial flexibilities into investment projects in planted forests. We use empirical data from three production cycles of forests planted with Eucalyptus grandis × E. urophylla in the projection of discounted cash flows. The Eucalyptus wood price, assumed as uncertainty, was modeled using fractional and geometric Brownian motion. The discrete-time pricing of European options was obtained using the Monte Carlo method. The root mean square error of fractional and geometric Brownian motions was USD 1.4 and USD 2.2, respectively. The real options approach gave the investment projects, with fractional and geometric Brownian motion, an expanded present value of USD 8,157,706 and USD 9,162,202, respectively. Furthermore, in both models, the optimal harvest ages execution was three rotations. Thus, with an indication of overvaluation of 4.9% when assimilating the geometric Brownian motion, there is no parity between stochastic processes, and three production cycles of Eucalyptus planted forests are economically viable. Full article

Utilizing gauge symmetries, the Geometric Arbitrage Theory reformulates any asset model, allowing for arbitrage by means of a stochastic principal fibre bundle with a connection whose curvature measures the “instantaneous arbitrage capability”. The cash flow bundle is the associated vector bundle. The zero eigenspace of its connection Laplacian parameterizes all risk-neutral measures equivalent to the statistical one. A market satisfies the No-Free-Lunch-with-Vanishing-Risk (NFLVR) condition if and only if 0 is in the discrete spectrum of the Laplacian. The Jarrow–Protter–Shimbo theory of asset bubbles and their classification and decomposition extend to markets not satisfying the NFLVR. Euler’s characteristic of the asset nominal space and non-vanishing of the homology group of the cash flow bundle are both topological obstructions to NFLVR. Full article

For informing future energy policy decisions, it is essential to choose the correct social discount rate (SDR) for ex-ante economic evaluations. Generally, costs and benefits—both economic and environmental—are weighted through a single constant discount rate. This leads to excessive discounting of the present value of cash flows progressively more distant over time. Evaluating energy projects through constant discount rates would mean underestimating their environmental externalities. This study intends to characterize environmental–economic discounting models calibrated for energy investments, distinguishing between intra- and inter-generational projects. In both cases, the idea is to use two discounting rates: an economic rate to assess financial components and an ecological rate to weight environmental effects. For intra-generational projects, the dual discount rates are assumed to be constant over time. For inter-generational projects, the model is time-declining to give greater weight to environmental damages and benefits in the long-term. Our discounting approaches are based on Ramsey’s growth model and Gollier’s ecological discounting model; the latter is expressed as a function of an index capable of describing the performance of a country’s energy systems. With regards to the models we propose, the novelty lies in the calibration of the “environmental quality” parameter. Regarding the model for long-term projects, another innovation concerns the analysis of risk components linked to economic variables; the growth rate of consumption is modelled as a stochastic variable. The defined models were implemented to determine discount rates for both Italy and China. In both cases, the estimated discount rates are lower than those suggested by governments. This means that the use of dual discounting approaches can guide policymakers towards sustainable investment in line with UN climate neutrality objectives. Full article

This study concerns the use of the critical chain method to schedule the construction of renewable energy facilities. The critical chain method is recognized as a useful project management tool, transforming a stochastic problem of uncertainty in activity durations into a deterministic one. However, this method has some shortcomings. There are no clear principles of grouping non-critical activities into feeding chains. Another ambiguity is sizing the feeding buffers with regard to the topology of the network model and the resulting dependencies between activities, located in different chains. As a result, it is often necessary to arbitrarily adjust the calculated sizes of feeding buffers before inserting them into the schedule. The authors present the new approach to sizing the time buffers in the schedule, enabling a quick assessment of the quality of a given solution variant and finding a solution that best meets the established criteria, conditions, and constraints. The essence of the presented approach is the two-step sizing of time buffers with the use of deterministic optimization and stochastic optimization techniques. Taking into account construction management needs, the optimization criteria are based on the construction project cash flow analysis. The effectiveness of the presented approach is illustrated by an example of developing a wind power plant construction schedule. According to the results, the presented approach ensures the protection of the scheduled completion date of the construction and the stability of the schedule. Full article