Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi

Linking Agent-Based Models and Stochastic Models of Financial Markets (CROSBI ID 190736)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Feng, Ling ; Li, Baowen ; Podobnik, Boris ; Preis, Tobias ; Stanley, Eugene H. Linking Agent-Based Models and Stochastic Models of Financial Markets // Proceedings of the National Academy of Sciences of the United States of America, 109 (2012), 22; 8388-8393. doi: 10.1073/pnas.1205013109

Podaci o odgovornosti

Feng, Ling ; Li, Baowen ; Podobnik, Boris ; Preis, Tobias ; Stanley, Eugene H.

engleski

Linking Agent-Based Models and Stochastic Models of Financial Markets

It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that “fat” tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.

finance; complex systems; power law; scaling laws; agent-based modeling

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o izdanju

109 (22)

2012.

8388-8393

objavljeno

0027-8424

10.1073/pnas.1205013109

Povezanost rada

Fizika, Ekonomija

Poveznice
Indeksiranost