Production Decision Analysis Under Exchange Rate Demand, And Carbon Prices Uncertainties
Bibliographic record
Abstract
This thesis presents an optimal production decision analysis for a multinational firm under exchange rate, carbon allowance prices, and demand uncertainties. Firms having production and sales in two different countries experience both demand and exchange rate uncertainties. When exchange rates move unfavorably, multinational firms face financial losses because of falling profits. Demand uncertainties may result in underage cost when production quantities are less than the demand, or overage cost when production quantities are more than the demand. Additionally, recent environmental regulations on emissions of green house gases, particularly carbon dioxide emissions, also pose risk on firms’s profitability. It is thus important for a risk-averse manager to decide how to mitigate these uncertainties to protect the firm’s financial losses. In order to address these issues, mathematical models that capture firm’s production allocation problem under different scenarios of exchange rate, carbon emissions, and demand uncertainties have been developed. The risk attitude of the firm manager is assumed to be risk averse and is modeled by a mean-variance (MV) utility function. In order to hedge downside risk of exchange rates and upside risk of carbon allowance prices, the firm takes long positions in currency put and carbon call options, respectively. The objective is to maximize the MV function of the firm subject to various capacity and demand constraints and determine the optimal number of currency put and carbon call options. The firm possesses real options capability in the form of capacity flexibility represented by a vector of discrete capacity levels to meet uncertainties of demand. Demand uncertainties are assumed to follow regime-switching behaviors – considering both onestate and two-state probability distributions. The stochastic behavior of exchange rate is modeled by a geometric Brownian motion and its limiting case as a random walk. Functioning under a cap-and-trade emission trading scheme, the firm is obliged to buy carbon allowances for its carbon emissions. Carbon allowance prices are modeled as both geometric Brownian motion and geometric Brownian motion with jump processes. Results demonstrate that integration of real options and financial options increases the utility of the firm, while financial options reduce the variance of the profit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".