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Complex interactions in financial markets

Finger, Karl - 2014

Persistent link: https://www.econbiz.de/10011305495

Relative forecasting performance of volatility models : Monte Carlo evidence

Lux, Thomas; Morales-Arias, Leonardo - 2010

A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized Autoregressive Conditional Heteroskedasticity...

Persistent link: https://www.econbiz.de/10003932329

Parameter estimation and forecasting for multiplicative lognormal cascades

Leövey, Andrés E.; Lux, Thomas - 2011

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...

Persistent link: https://www.econbiz.de/10009389845

Multifractal models, intertrade durations and return volatility

Segnon, Mawuli - 2015

Persistent link: https://www.econbiz.de/10011299266

An analysis of systematic risk in worldwide econonomic sentiment indices

Luu, Duc Thi; Yanovski, Boyan; Lux, Thomas - 2018 - Ver. January 2, 2018

tools of Random Matrix Theory (RMT) and Principal Component Analysis (PCA), our paper aims to extract latent information … embedded in the interactions between economic and business sentiment indices around the world. We find that: (i) The dynamics … collapse of the US housing market in 2007, can lead to a homogenization of the expectation structure around the world, as such …

Persistent link: https://www.econbiz.de/10011790790

Modeling and forecasting crude oil price volatility : evidence from historical and recent data

Lux, Thomas; Segnon, Mawuli; Gupta, Rangan - 2015

This paper uses the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 and from January 03, 1977 to March 24, 2014. Based on...

Persistent link: https://www.econbiz.de/10010488966

Scaling and criticality in a stochastic multi-agent model of a financial market

Lux, Thomas; Marchesi, Michele - 1998

Persistent link: https://www.econbiz.de/10001372561

Testing for non-linear structure in an artificial financial market

Chen, Shu-Heng; Lux, Thomas; Marchesi, Michele - 1999

Persistent link: https://www.econbiz.de/10001372680

Bringing an elementary agent-based model to the data : estimation via GMM and an application to forecasting of asset price volatility

Ghonghadze, Jaba; Lux, Thomas - 2015

We explore the issue of estimating a simple agent-based model of price formation in an asset market using the approach of Alfarano et al. (2008) as an example. Since we are able to derive various moment conditions for this model, we can apply generalized method of moments (GMM) estimation. We...

Persistent link: https://www.econbiz.de/10010501932

Modeling and forecasting carbon dioxide emission allowance spot price volatility : multifractal vs. GARCH-type volatility models

Segnon, Mawuli; Lux, Thomas; Gupta, Rangan - 2015

This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH)...

Persistent link: https://www.econbiz.de/10011296114