Predicting Asset Dynamics with Hybrid Bivariate Kernel Density Estimate and Markov Model

Mantas Landauskas (), Tomas Ruzgas () and Eimutis Valakevičius ()
Additional contact information
Mantas Landauskas: Kaunas University of Technology
Tomas Ruzgas: Kaunas University of Technology
Eimutis Valakevičius: Kaunas University of Technology

Computational Economics, 2025, vol. 66, issue 1, No 13, 405-419

Abstract: Abstract There exists a variety of mathematical models for the prediction of asset dynamics. Some of them are based on special assumptions for the data, such as the normal distribution of logarithms of the total return rates, nonnegative asset values, etc. By using the term "asset", we refer to time-varying data such as currency exchange rates, stock prices, etc. In this work, we present a universal approach to the prediction of asset dynamics which does not require the normality assumption, i.e., the data may follow any type of probability distribution. The presented method employs the two-dimensional kernel density estimate for asset return rates. In this sense, the resulting bivariate random variable estimates the probability distribution of the asset return rates. The distribution is then used to directly simulate asset dynamics or acts as a tool to estimate the transition matrix for the proposed discrete hybrid model. The results show that the hybrid approach yields a smaller prediction error for shorter time horizons, i.e., spanning 1 month or less. The corresponding prediction error is below 1%. Should one need to simulate longer time periods, additional model tuning is required, mainly by testing transition matrices of bigger dimensions.

Keywords: Forecasting; Currency; Asset price; Kernel density; Monte Carlo; 62G07; 62P05; 65C05 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10721-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10721-2

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10721-2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().