Bibliographic record
Abstract
This thesis consists of five chapters that examines risk and uncertainty within two frameworks: foreign exchange market and real options. The first chapter is a preliminary part that overviews the structure of thesis. In the second chapter, I examine the impact of scheduled macroeconomic announcements on realised variance in the Canadian dollar/US dollar foreign exchange market. Information shocks as a whole are made up of public information shocks and private information shocks. I measure the public information shocks from the analyst forecast surprise and the private information shocks from volatility sensitivity to liquidity variables. I find that the realized variance is driven mainly by the latter rather than the former. However, my results for the most important announcements are not significant, which might be due to these being well-analysed publicly. Spread, as a proxy of private information shocks, is the most important liquidity measure, showing a significant increase around the arrival of announcements. My results are robust to joint effects of liquidity variables, considering announcements throughout the day (times other than 8:30 announcement), alternative measures of volatility (absolute return and modified absolute return), evaluation of announcements for US and Canada separately, examine the impact of surprise in model, and the economic classification of announcements. In the third chapter, I aim to evaluate risk and uncertainty using real options technique. I develop a framework to evaluate representative agents’ behaviour in a real options switching framework. I set up three models with revertible switching process under uncertainty and solve these using the alternating direction implicit algorithm. The models break down into: cash-cost model, cash-time model, and projection model. The cash-cost model captures the cash expenses of switching whereas the cash-time model not only captures the cash cost but also the exact time cost, which is critical in horticulture. The projection model presents an approximation of cash-time model that has less computational complexity. The results of my sensitivity analyses indicate that increases in cost, time, volatility, drift, and discount rate have negative impacts on the switch frequency. If the correlation between two crops is positive, it has negative impacts on switch frequency, otherwise it has positive impacts. Differences between the models are more pronounced over longer periods. In the fourth and fifth chapters, I extend the cash-time model from chapter three to evaluate orchardists’ behaviour in the Hawke’s Bay region. Chapter four examines the dataset thoroughly and provide a statistical review of orchards that will be modeled in chapter five. Orchardists have the incentive to switch from one type of apple to another as the apple profits change. In my model, orchardists have the option to carry on with the existing apple trees or to switch to competing apple types by uprooting the existing apple trees and planting new ones or grafting on the existing rootstock. The uprooting strategy is relatively expensive but is instantaneous, and results in young (unproductive) apple trees with a long life ahead of them. In contrast, the grafting strategy is less expensive and faster but continues with old trees. I compute the optimal land value at each age of apple trees from one-year to 33-years old. My results show that grafting is the optimal strategy when trees are young, whereas planting becomes optimal when they are old. Examining the apple dataset, I find that orchardists are biased against uprooting and grafting relative to my predictions. The deviation from what my model proposes and what orchardists follow in reality might be due to the assumption of my model and possible factors in the orchards that my model does not capture. My results show that the deviation from optimal policy for small orchardists is not significantly different from large orchardists.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".