arXiv subjects

Rafał Sieradzki

Publications and source records attributed to Rafał Sieradzki.

3 recordsLinked to original sources

arXiv2026

Same Firms, Different Verdicts: ESG Rating Choice and the Measurement of Greenwashing

This paper investigates the Aggregate Confusion hypothesis (Berg, Kolbel, and Rigobon, 2022) at the firm level by measuring the Disclosure-Performance Gap (DPG), the standardised divergence between a firm's voluntary environmental disclosure ("Talk") and its realised emissions performance ("Walk"). The sample comprises 200 large European firms from the Energy, Materials, Industrials, and Utilities sectors of the STOXX Europe 600 in fiscal year 2023, the final cross-section of the voluntary reporting era before the Corporate Sustainability Reporting Directive. The model is selected through a six-stage process, candidate assembly, correlation screening, VIF based multicollinearity filtering, stepwise forward search under the corrected Akaike Information Criterion, Cook's distance screening, and HC3 re-estimation across 421 candidate specifications, estimated by ordinary least squares with HC3 robust standard errors on the full sample. Flagship index membership is the strongest predictor of a wider gap ($β$ = +0.78, p < 0.01), consistent with institutional ceremonial conformity. TCFD endorsement is also positive ($β$ = +0.86, p < 0.05) but identified off a small group of non-supporting firms, so it is read as directional, not a precise magnitude. Renewable energy use ($β$ = -0.31, p < 0.01) and environmental capital expenditure ($β$ = -0.22, p < 0.05) significantly narrow the gap, consistent with signalling theory, while governance and monitoring variables carry no explanatory power. Results are robust to influence trimming, rank-based recoding of the disclosure score, and removal of the TCFD variable. Replacing the CDP Climate Score with the LSEG Environmental Pillar Score eliminates the index-membership effect while the renewable-energy effect survives, showing that detected greenwashing is conditional on the rating lens applied.

q-fin.GN↗

arXiv2026

How to spot outliers: an Ensemble Anomaly Detection Framework

Errors in risk valuation outputs arising from data-feed failures, model misconfiguration, or system malfunctions can propagate undetected through an investment bank's risk infrastructure and generate material operational losses. Using proprietary daily credit-derivatives data from a major global investment bank covering 183 trades across 129 trading days, we design, implement, and empirically evaluate the Ensemble Quality Assessment Framework (EQAF), a layered unsupervised architecture that combines complementary outlier-detection methods to monitor risk calculation integrity in real time. Using a controlled anomaly-injection protocol with eight operationally realistic scenarios, we show that the calibrated ensemble achieves F1 scores of 61-79%, substantially outperforming the best individual method (6-66%) across four distinct risk-measure datasets. Improvements of 4-6 percentage points in AUC-ROC confirm that this advantage is robust to threshold selection. We further demonstrate that purely statistical detection methods systematically fail to identify stale-value anomalies, a class of frozen-feed errors in which valuation outputs are identical to prior observations and therefore indistinguishable from normal data, and that domain-specific deterministic rules are architecturally indispensable. These findings have direct implications for model risk management under Basel III and the Fundamental Review of the Trading Book (FRTB), where automated and auditable quality controls for internal risk models are increasingly required.

q-fin.RM↗

arXiv2023

Systemic risk indicator based on implied and realized volatility

We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2023 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic that the simpler is always better than the more complex if it leads to the same results. Such an approach significantly limits model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise and valid measure of the current systemic risk in the stock markets. Moreover, it can be used for other types of assets and high-frequency data. The forecasting ability of various SRIs (including CATFIN, CISS, IVRVSRI, SRISK, and Cleveland FED) with regard to weekly returns of S&P 500 index is evaluated based on the simple linear, quasi-quantile, and quantile regressions. We show that IVRVSRI has the strongest predicting power among them.

q-fin.RM↗