2014 D+H Polska - nowe rozwiazania w systemach sygnalizacji pożaruD+H Polska
Nowa jakość sygnalizacji pożaru na przykładzie rozwiązań D+H Polska / Protec. Centrale pożarowe Protec 6100 do małych i średnich obiektów oraz centralę pożarową Protec 6400. Poznaj protokół Protec Algo-Tec™ 6000PLUS .
2014 D+H Polska - nowe rozwiazania w systemach sygnalizacji pożaruD+H Polska
Nowa jakość sygnalizacji pożaru na przykładzie rozwiązań D+H Polska / Protec. Centrale pożarowe Protec 6100 do małych i średnich obiektów oraz centralę pożarową Protec 6400. Poznaj protokół Protec Algo-Tec™ 6000PLUS .
Clustering CDS: algorithms, distances, stability and convergence ratesGautier Marti
Talk given at CMStatistics 2016 (http://cmstatistics.org/CMStatistics2016/).
The standard methodology for clustering financial time series is quite brittle to outliers / heavy-tails for many reasons: Single Linkage / MST suffers from the chaining phenomenon; Pearson correlation coefficient is relevant for Gaussian distributions which is usually not the case for financial returns (especially for credit derivatives). At Hellebore Capital Ltd, we strive to improve the methodology and to ground it. We think that stability is a paramount property to verify, which is closely linked to statistical convergence rates of the methodologies (combination of clustering algorithms and dependence estimators). This gives us a model selection criterion: The best clustering methodology is the methodology that can reach a given 'accuracy' with the minimum sample size.
On the stability of clustering financial time seriesGautier Marti
Talk at IEEE ICMLA 2015 Miami
In this presentation, we suggest some data perturbations that can help to validate or reject a clustering methodology besides yielding insights on the time series at hand. We show in this study that Pearson correlation is not that relevant for clustering these time series since it yields unstable clusters; prefer a more robust measure such as Spearman correlation based on rank statistics.
Clustering CDS: algorithms, distances, stability and convergence ratesGautier Marti
Talk given at CMStatistics 2016 (http://cmstatistics.org/CMStatistics2016/).
The standard methodology for clustering financial time series is quite brittle to outliers / heavy-tails for many reasons: Single Linkage / MST suffers from the chaining phenomenon; Pearson correlation coefficient is relevant for Gaussian distributions which is usually not the case for financial returns (especially for credit derivatives). At Hellebore Capital Ltd, we strive to improve the methodology and to ground it. We think that stability is a paramount property to verify, which is closely linked to statistical convergence rates of the methodologies (combination of clustering algorithms and dependence estimators). This gives us a model selection criterion: The best clustering methodology is the methodology that can reach a given 'accuracy' with the minimum sample size.
On the stability of clustering financial time seriesGautier Marti
Talk at IEEE ICMLA 2015 Miami
In this presentation, we suggest some data perturbations that can help to validate or reject a clustering methodology besides yielding insights on the time series at hand. We show in this study that Pearson correlation is not that relevant for clustering these time series since it yields unstable clusters; prefer a more robust measure such as Spearman correlation based on rank statistics.