Weighted-indexed semi-Markov model: calibration and application to financial modeling

نویسندگان

چکیده

Abstract We address the calibration issues of weighted-indexed semi-Markov chain (WISMC) model applied to high-frequency financial data. Specifically, we propose automate discretization price returns and volatility index by using four different approaches, two based on statistical quantities, namely, quantile sigma discretization, derived application popular machine learning algorithms, namely k-means Gaussian mixture (GMM). Moreover, comparing Bayesian information criterion (BIC) scores, GMM approach allows for selection number states index. An Bitcoin prices at 1-min 1-s intervals shows validity usefulness proposed approaches. In particular, is well suited returns, whereas works better low-frequency intervals. Finally, results Monte Carlo simulation, show that WISMC model, with can reproduce long-range serial correlation squared which typical markets and, in cryptocurrency market.

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ژورنال

عنوان ژورنال: Financial Innovation

سال: 2023

ISSN: ['2199-4730']

DOI: https://doi.org/10.1186/s40854-022-00418-6