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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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11 results for ECB

New models analyze how ECB's unconventional policies affect stock market volatility.

problem Analyzing the impact of ECB's unconventional policies on stock market volatility.
method Developed MEM with Asymmetry and Policy effects (MAP) models to separate base volatility from policy effects.
result Significant improvement in forecasting power after Expanded Asset Purchase Programme implementation.

Paper details how to smoothly transition from EONIA to ESTR without significant financial impact.

problem Transition from EONIA to ESTR impacts financial instruments, especially OTC derivatives.
method Detailed analysis of how clean discounting approach based on ESTR affects pricing of OIS, IRS, and XVAs.
result The transition to EONIA-free pricing framework is safe and consistent, ensuring complete elimination of EONIA.

We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quanti…

2019-05-04abs ↗pdf ↗

Study compares empirical systemic risk with balance sheet risk in interbank networks.

problem Disentangling balance sheet risk from network effects in systemic risk.
method Generalised DebtRank dynamics and maximum-entropy approach to compare observed and expected systemic risk.
result Systemic risk levels are compatible but differ significantly during turbulent times.

Machine learning models predict housing prices using macroeconomic factors.

problem Predicting housing prices using macroeconomic data.
method Used machine learning (kNN and tree-bagging) on a dataset of macroeconomic factors.
result Machine learning models can predict housing prices with uncertainties better than existing index uncertainties.

Machine learning models outperform traditional econometric methods for forecasting term structure of government bonds

problem Forecasting the term structure of government bonds
method Combining traditional econometric models with neural network architectures
result Neural network models consistently outperform traditional models in both forecasting accuracy and portfolio performance