Connectedness between Sectors: The Case of the Polish Stock Market before and during COVID-19
Bibliographic record
Abstract
This article studies the connectedness between economic sectors of the Polish stock market. The sectors that are considered are the following: banks, basic materials, chemicals, construction, developers, energy, food, and oil and gas. The analysis of the connectedness among sectors is conducted from a statistical and dynamic perspective. Using the time-varying parameter vector autoregression (TVP-VAR) method, the intensity, direction and variation of volatility spillover between the economic sectors are studied. Two samples are analysed, the first one being from 1 January 2013 to 12 December 2019, which corresponds to the period before the pandemic caused by COVID-19, and the second one being from 1 January 2020 to 2 December 2021, which corresponds to the period during the pandemic. A series of results are obtained. First, the connectedness between the economic sectors varies depends on the time. Second, the connectedness between the sectors was stronger during the crisis caused by the outbreak of COVID-19 rather than before the crisis. The volatility of each sector was also primarily due to their own volatility. Thirdly, the banking sector was the main sector with respect to volatility spillover. The results that are obtained are important for making the right decisions regarding financial stability under crisis circumstances, when considering development strategies for some economic sectors but also in portfolio management for performing diversification and risk-mitigation strategies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".