2008/09/23 by Alessandro De Gregorio, Stefano M. Iacus, De Gregorio, Alessandro +2
Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Financial Risk and Volatility Modeling #Machine Learning (stat.ML) #Methodology (stat.ME) #Probability (math.PR) #Statistical Finance (q-fin.ST) #Stochastic processes and financial applications #math.PR #q-fin.ST #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.0809.3902
arxiv created 2008/09/23 · openalex publication_date 2008/09/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper a new dissimilarity measure to identify groups of assets dynamics is proposed. The underlying generating process is assumed to be a diffusion process solution of stochastic differential equations and observed at discrete time. The mesh of observations is not required to shrink to zero. As distance between two observed paths, the quadratic distance of the corresponding estimated Markov operators is considered. Analysis of both synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence that this distance seems capable to catch differences in both the drift and diffusion coefficients contrary to other commonly used metrics.