Bibliographic record
Abstract
Purpose Prior literature has shown that, theoretically, holding-period returns of a leveraged exchange-traded fund (LETF) are generally negatively affected by the volatility of the underlying benchmark’s daily returns, particularly for long holding periods. However, recent empirical studies simulate LETFs’ returns using historical benchmark returns and report results that are not entirely consistent with the theoretical predictions, leading to the possibility that the distribution of real-world returns may have certain characteristics that influence the outcomes. In this paper, the authors examine how asymmetric volatility affects LETFs’ performance and provide detailed explanations for the behavior of the performance of LETFs under different market conditions. Design/methodology/approach The authors conduct simulation analyses on a +3x LETF and a −3x LETF based on historical S&P 500 stock index returns, with asymmetric volatility incorporated into the model. Findings By incorporating the asymmetric volatility effect, the simulation results suggest that, contrary to the theoretical predictions, higher volatility does not always lead to more negative impact on LETFs’ performance. Rather, the performance depends on the market conditions under which high volatility occurs. The findings therefore help reconcile prior theoretical predictions with reported empirical findings. Originality/value The analysis adds to the literature by incorporating the asymmetric volatility effect of stock returns in studying LETFs’ performance. The authors also provide detailed explanations for the behavior of LETFs’ returns and compounding effect under different market conditions, thus providing contexts to prior empirical results.
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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 | 0.000 |
| 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) | 0.001 | 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".