The paper proposes a framework for the relationship between bank- and trading desk-level market risk measurement, providing a method to model market risk appetite, position agnostic. This is used to assess the relationship between a portfolio’s market risk measurement and its position sensitivities and discuss the impact of external market volatility and cumulative market returns on market risk appetite allocation. The paper also provides a simplified illustrative example and discuss other practical applications of the framework for market risk appetite allocation, limit setting, stress and scenario analysis.

In this study, we address the inherent limitations of traditional rule-based surveillance

systems in financial markets by proposing an innovative approach that integrates concepts from econophysics with advanced Machine Learning techniques to enhance the adaptability and efficacy of market surveillance tools. Our findings demonstrate that the incorporation of physical measures significantly reduces false positives and enhances the model’s ability to detect meaningful market anomalies, particularly those associated with significant news events.

Previous literature on the market valuation of knowledge assets has to a large extent analysed R&D investment, advertising expenditures, and other IP assets such as patents and trademarks, while overlooking the appraisal of how design IP rights contribute to the value of a firm. Furthermore, the analysis of IP indicators appraising the value heterogeneity of design IP rights is almost an unexplored topic. This paper aims to fill this gap by devising a composite value index combining multiple indicators regarding the quality of a patent’s prior art, the patent prosecution characteristics, and the economic impact of the patent. Regression analysis of a patent’s enforcement value demonstrated that the proposed composite value index showed a satisfactory explanatory power as compared to when the indicators are analysed as such. The firm-level analysis employing Tobin’s q model confirmed that the proposed composite value index of design patent indicators is positively and significantly correlated with a firm’s market value above and beyond other knowledge assets of the firm, including R&D investment, advertising expenditures, patents, trademarks, and relative value indices of IP assets.

The European banking regulation allows financial institutions to choose between overlapping and non-overlapping one-year time windows to calculate the long run average of one-year de-fault rates (LRADR). Based on the distribution of the chosen LRADR, financial institutions must add a margin of conservatism for the general estimation error (MoC C) to their probability of default estimates. While existing literature addresses MoC C quantification for non-overlapping one-year default rates, there is a research gap for overlapping one-year default rates. The pur-pose of this paper is twofold. Starting from the assumption of independent numbers of default events in distinct periods, we first derive an equation for the MoC C for overlapping one-year default rates. Specifically, we propose an adjustment factor to convert the MoC C for non-overlapping into a MoC C for overlapping one-year default rates. Second, we demonstrate that the common practice of using the maximum likelihood estimator of asset correlation on sam-ples with different probabilities of default results in a downward bias. We compensate for this downward bias using a simulation approach. Our finding is also pertinent to economic capital quantification, as asset correlations feed into credit portfolio models in the second pillar of the Basel Capital Accord.