Mathematical Finance Company – Canadian Parameters – September 1999 Stochastic Asset Generators for Investment Portfolios of Bonds, Stocks, Real Estate, Commodities, and Foreign Assets Based on the Double Mean Reverting Process™ and the Vector Autoregress
Bibliographic record
Abstract
This paper considers a stochastic process on asset returns and stochastic asset return generators on stock indices, bond indices and other stock like indices such as real estate. It gives a model of the joint interest rate and investment indices process. This n-dimensional model can be used to model stock like indices such as the TSE 300, a managed stock index, an index invested in stocks and bonds, a foreign investment index, or all of these together. The n-dimensional process allows for having correlations between the different indices, as well as modeling inter-relationships in the conditional expected return based on the elements of the state vector modeled in the process. Commodities and other investment categories like junk bonds can be modeled as equity like indices. This generator can be applied to modeling guarantees on investment products in any of the asset categories. It can be used to model these guarantees when the customer has the ability to change the investment mix over time or to switch the investment mix. This generator can also be used to analyze the asset allocation decision for a company’s investment portfolio. The most difficult part in developing a stochastic asset generator for this set of investments is developing the interest rate model. The interest rate model must be arbitrage free, i.e. no free
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.029 |
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".