Nichtstationarität als ein zentraler Aspekt von Finanzmärkten
Bibliographic record
Abstract
We leverage methods from statistical physics to study problems in economics, particularly financial markets. While there are some examples in history where physicists contributed to problems in economics, both sciences developed independently.</br> The interdisciplinary field of econophysics has been formed during the last twenty years to facilitate the transfer of methods. We start by investigating the influence of the non-stationarity in financial time series on portfolio optimization and assess different methods designed to suppress the negative effects on the covariance estimation. The study compares different models to estimate the covariance matrix and how combinations of refinements can improve on them. The effectiveness of the refinements depends on the covariance estimators and they are essential to receive good results for portfolio optimization.</br> The temporal dependencies inherent in financial time series are investigated with a recently introduced quantile-based correlation function. The results provide a much broader overview of the time series’ features compared to the classic method of studying the autocorrelation of the absolute or squared returns. In addition, we study how well different common stochastic processes capture the features of empirical time series and find striking differences. To model the influence of the non-stationarity, we use an ensemble approach to construct a multivariate correlation-averaged normal distribution, which addresses the non-stationarity of the covariance matrix. We carry out an extensive empirical study to validate the approach.</br> The correlation-averaged normal distribution is then used as a realistic distribution for the asset values in the Merton model. We calculate the average loss distribution which takes the non-stationarity into account. This approach yields a quantitative understanding of why the benefits of diversification are limited. As practitioner-oriented risk measures we investigate the Value at Risk and Expected Tail Loss for credit portfolios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".