Author: Miguel Ataurima ArellanoPublisher:
ISBN:
Category :
Languages : en
Pages : 56
Book Description
Empirical Modeling of Latin American Stock and Forex Markets Returns and Volatility Using Markov-Switching Garch Models
Author: Miguel Ataurima ArellanoPublisher:
ISBN:
Category :
Languages : en
Pages : 56
Book Description
Empirical modelling of latin american stock markets returns and volatility using Markov - Switching garch models
Author: Miguel Ataurima ArellanoPublisher:
ISBN:
Category :
Languages : es
Pages :
Book Description
Modeling Latin-American Stock and Forex Markets Volatility
Author: Gabriel RodriguezAn Empirical Application of a Random Level Shifts Model with Time-varying Probability and Mean Reversion to the Volatility of Latin-American Forex Markets Returns
Author: José Carlos Gonzáles TanakaPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Stock Market Anomalies
Author: Victor Silverio Posadas HernandezPublisher: Springer Science & Business Media
ISBN: 3835091034
Category : Business & Economics
Languages : en
Pages : 205
Book Description
Victor Silverio Posadas Hernandez explores three sets of questions: What are the investment laws in the Latin American emerging markets (LAEM) and how do they compare to those of developed countries? How heterogeneous are the implicit trading costs in the LAEM and which factors are responsible for the heterogeneity? How does the predictability of stock returns in the LAEM differ from those documented for developed markets?
A Markov-Switching Equilibrium Correction Model for Intraday Futures and Stock Index Returns
Author: Xavier GiroudPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
A considerable literature in market microstructure has analyzed the information flows between stock index futures and spot markets. Most of those studies estimate deviations from the cost-of-carry model within the framework of vector equilibrium correction models (VECM). The typical finding is that futures prices lead spot prices and are the primary source of price discovery. Purely linear models can, however, lead to fallacious or at least incomplete inference in the presence of significant nonlinearities in the return generating process. Recent research has reported evidence for nonlinearity in the distribution of stock market returns. According to this literature, their empirical distribution can be characterized by a mixture of normal distributions whose dependence is well described by a hidden Markov chain. This thesis contributes to the former field by allowing for Markovian regime switches in the cointegrated system. The empirical analysis is carried out using high-frequency data for the German and Swiss markets, i.e. two closely interrelated markets which differ substantially in terms of liquidity. This thesis consists of three major parts. In the first part, an MS-VECM is estimated for each market and tested against the linear VECM. In both cases, the linear model is strongly rejected. The Markovian chain consists of three regimes, which can be well described in terms of volatility. Price discovery differs from regime to regime, but the overall evidence is consistent with the well-documented leading role of futures markets. The MS-VECM provides additional insights into the dynamics of price discovery. Interestingly, shocks are absorbed more rapidly in regimes of high volatility. A possible explanation is provided, based on trading activity. Intraday volatility is shown to be associated with the volume of trading. Heavy trading reveals more information per unit of time and thus improves index arbitrage and informational.
An Empirical Application of Stochastic Volatility Models to Latin-American Stock Returns Using GH Skew Student's T-distribution
Author: Modeling Latin-American Stock Markets Volatility
Author: Financial Risk Management with Bayesian Estimation of GARCH Models
Author: David ArdiaPublisher: Springer Science & Business Media
ISBN: 3540786570
Category : Business & Economics
Languages : en
Pages : 206
Book Description
This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Author: Carlos A. Abanto-Valle