Market probability density functions and investor risk aversion for the australia-us dollar exchange rate.
Bibliographic record
Abstract
This thesis models the Australian-US Dollar (AUD/USD) exchange rate with particular attention being paid to investor risk aversion. Accounting for investor risk aversion in AUD/USD exchange rate modelling is novel, so too is the method used to measure risk aversion in this thesis. Investor risk aversion is measured using a technique developed in Bliss and Panigirtzoglou (2004), which makes use of Probability Density Functions (PDFs) extracted from option markets. More conventional approaches use forward-market pricing or Uncovered Interest Parity. Several methods of estimating PDFs from option and spot markets are examined, with the estimations from currency spot-markets representing an original application of an arbitrage technique developed in Stutzer (1996) to the AUD/USD exchange rate. The option and spot-market PDFs are compared using their first four moments and if estimated judiciously, the spot-market PDFs are found to have similar shapes to the option-market PDFs. So in the absence of an AUD/USD exchange rate options market, spot-market PDFs can act as a reasonable substitute for option-market PDFs for the purpose of examining market sentiment. The Relative Risk Aversion (RRA) attached to the AUD/USD, the US Dollar-Japanese Yen, the US Dollar-Swiss Franc and the US-Canadian Dollar exchange rates is measured using the Bliss and Panigirtzoglou (2004) technique. Amongst these exchange rates, only the AUD/USD exchange rate demonstrates a significant level of investor RRA and only over a weekly forecast horizon. The Bliss and Panigirtzoglou (2004) technique is also used to approximate a time-varying risk premium for the AUD/USD exchange rate. This risk premium is added to the cointegrating vectors of fixed-price and asset monetary models of the AUD/USD exchange rate. An index of Australia’s export commodity prices is also added. The out-of-sample forecasting ability of these cointegrating vectors is tested relative to a random walk using an error-correction framework. While adding the time-varying risk premium improves this forecasting ability, adding export commodity prices does so by more. Further, including both the time-varying risk premium and export commodity prices in the cointegrating vectors reduces their forecasting ability. So the time-varying risk premium is important for AUD/USD exchange rate modelling, but not as important as export commodity prices.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".