Modelling and forecasting the kurtosis and returns distributions of financial markets: irrational fractional Brownian motion model approach

This paper reports a new methodology and results on the forecast of the numerical value of the fat tail(s) in asset returns distributions using the irrational fractional Brownian motion model. Optimal model parameter values are obtained from fits to consecutive daily 2-year period returns of S&P...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Annals of operations research Jg. 299; H. 1-2; S. 1397 - 1410
Hauptverfasser: Dhesi, Gurjeet, Shakeel, Bilal, Ausloos, Marcel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2021
Springer
Springer Nature B.V
Schlagworte:
Brownian motion
Business and Management
Combinatorics
Distribution
Econometrics
Economic forecasting
Economic models
Efficient markets
Financial instruments
Financial markets
Forecasts and trends
Kurtosis
Management
Mathematical models
Normal distribution
Operations research
Operations Research/Decision Theory
Parameters
Random variables
Regression analysis
Return on assets
S.I. : Recent Developments in Financial Modeling and Risk Management
Securities markets
Theory of Computation
United Kingdom
Asset pricing (G17)
Financial econometrics (C58)
Financial forecasting and simulation (G12)
Simulation modelling (C63)
ISSN:0254-5330, 1572-9338
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper reports a new methodology and results on the forecast of the numerical value of the fat tail(s) in asset returns distributions using the irrational fractional Brownian motion model. Optimal model parameter values are obtained from fits to consecutive daily 2-year period returns of S&P500 index over [1950–2016], generating 33-time series estimations. Through an econometric model, the kurtosis of returns distributions is modelled as a function of these parameters. Subsequently an auto-regressive analysis on these parameters advances the modelling and forecasting of kurtosis and returns distributions, providing the accurate shape of returns distributions and measurement of Value at Risk.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03305-z