Nonparametric adaptive value-at-risk quantification based on the multiscale energy distribution of asset returns
Langue
EN
Communication dans un congrès avec actes
Ce document a été publié dans
28th European Signal Processing Conference (EUSIPCO), Nonparametric adaptive value-at-risk quantification based on the multiscale energy distribution of asset returns, 2021-01-18, Amsterdam. 2021, vol. 2021-January, p. 2393-2397
Résumé en anglais
Quantifying risk is pivotal for every financial institution, with the temporal dimension being the key aspect for all the well-established risk measures. However, exploiting the frequency information conveyed by financial ...Lire la suite >
Quantifying risk is pivotal for every financial institution, with the temporal dimension being the key aspect for all the well-established risk measures. However, exploiting the frequency information conveyed by financial data, could yield improved insights about the inherent risk evolution in a joint time-frequency fashion. Nevertheless, the great majority of risk managers make no explicit distinction between the information captured by patterns of different frequency content, while relying on the full time-resolution data, regardless of the trading horizon. To address this problem, a novel value-at-risk (VaR) quantification method is proposed, which combines nonlinearly the time-evolving energy profile of returns series at multiple frequency scales, determined by the predefined trading horizon. Most importantly, our proposed method can be coupled with any quantile-based risk measure to enhance its performance. Experimental evaluation with real data reveals an increased robustness of our method in efficiently controlling under-/overestimated VaR values.< Réduire
Mots clés en anglais
Commerce
Different frequency
Experimental evaluation
Financial institution
Frequency information
Multiple frequency
Multiscale energy
Quantification methods
Risk assessment
Signal processing
Temporal dimensions
Value engineering
Unités de recherche