Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Structural Vector Autoregressions with Heteroskedasticity : A Comparison of Different Volatility Models
Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Structural Vector Autoregressions : Checking Identifying Long-run Restrictions Via Heteroskedasticity
Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Choosing Between Different Time-varying Volatility Models for Structural Vector Autoregressive Analysis
Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Handbook of Volatility Models and Their Applications
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 0470872519
Category : Business & Economics
Languages : en
Pages : 566
Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Publisher: John Wiley & Sons
ISBN: 0470872519
Category : Business & Economics
Languages : en
Pages : 566
Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Structural Vector Autoregressions with Smooth Transition in Variances : The Interaction Between US Monetary Policy and the Stock Market
Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Structural Vector Autoregressive Models with More Shocks Than Variables Identified Via Heteroskedasticity
Author: Helmut Lütkepohl
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
In conventional structural vector autoregressive (VAR) models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. However, even if there is heteroskedasticity, the number of shocks that can be identified is limited. A number of results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
In conventional structural vector autoregressive (VAR) models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. However, even if there is heteroskedasticity, the number of shocks that can be identified is limited. A number of results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.
Structural Vector Autoregressive Analysis
Author: Lutz Kilian
Publisher: Cambridge University Press
ISBN: 1107196574
Category : Business & Economics
Languages : en
Pages : 757
Book Description
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Publisher: Cambridge University Press
ISBN: 1107196574
Category : Business & Economics
Languages : en
Pages : 757
Book Description
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Structural Vector Autoregressions with Markov Switching
Author: Aleksei Netšunajev
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 79
Book Description
Structural vector autoregressions are of great importance in applied macroeconometric work. The main di culty associated with structural analysis is to identify unique shocks of interest. In a conventional approach this is done via zero or sign restrictions. Heteroskedasticity is proposed for use in identi cation. Under certain assumptions when volatility of shocks changes over time, unique shocks can be obtained. Then formal testing of the restrictions and impulse response analysis can be performed. In this thesis I show how identi cation via heteroskedasticity can be used in di erent contexts. In the rst chapter I analyze the dynamics of trade balances in response to macroeconomic shocks. I show that identifying restrictions, which are known in the literature, are rejected for two out of seven countries. Partially identi ed models fail to provide enough information to fully identify shocks. The second chapter, coauthored with my supervisor, demonstrates how one can bene t from identi cation via heteroskedasticity when sign restrictions are used. The approach is illustrated with a model of the crude oil market. It is shown that shocks identi ed via previously known sign restrictions are in line with the properties of the data. Use of tighter restrictions uncovers that the approach can be discriminative. The third chapter reconsiders the con icting results in the debate on the e ects of technology shocks on hours worked. Using six ways of identifying technology shocks, I nd that not all of them are supported by the data. There is no clear-cut evidence in favor of positive reaction of hours to technology shocks. However, it is plausible for real wage and disentangled investment-speci c and neutral technology shocks, even though conventional identi cation of the latter shocks is rejected.
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 79
Book Description
Structural vector autoregressions are of great importance in applied macroeconometric work. The main di culty associated with structural analysis is to identify unique shocks of interest. In a conventional approach this is done via zero or sign restrictions. Heteroskedasticity is proposed for use in identi cation. Under certain assumptions when volatility of shocks changes over time, unique shocks can be obtained. Then formal testing of the restrictions and impulse response analysis can be performed. In this thesis I show how identi cation via heteroskedasticity can be used in di erent contexts. In the rst chapter I analyze the dynamics of trade balances in response to macroeconomic shocks. I show that identifying restrictions, which are known in the literature, are rejected for two out of seven countries. Partially identi ed models fail to provide enough information to fully identify shocks. The second chapter, coauthored with my supervisor, demonstrates how one can bene t from identi cation via heteroskedasticity when sign restrictions are used. The approach is illustrated with a model of the crude oil market. It is shown that shocks identi ed via previously known sign restrictions are in line with the properties of the data. Use of tighter restrictions uncovers that the approach can be discriminative. The third chapter reconsiders the con icting results in the debate on the e ects of technology shocks on hours worked. Using six ways of identifying technology shocks, I nd that not all of them are supported by the data. There is no clear-cut evidence in favor of positive reaction of hours to technology shocks. However, it is plausible for real wage and disentangled investment-speci c and neutral technology shocks, even though conventional identi cation of the latter shocks is rejected.
Three Essays on Long Memory Tests for Persistence in Volatility and Structural Vector Autoregression Modeling of Real Exchange Rates
Author: Osman Kubilay Gursel
Publisher:
ISBN:
Category :
Languages : en
Pages : 218
Book Description
In the first chapter the performance of two of the long memory tests, the Modified Rescaled Range Test and Geweke and Porter-Hudak Test for persistence in small samples is examined using Monte-Carlo methods. Some possible candidates for persistence in volatility are Autoregressive Conditional Heteroskedasticity (ARCH), Markov Regime Switching ARCH, and long memory. The long memory series are simulated through a Semi-Markov process with Pareto waiting times and lognormal realizations. The persistence in volatility arising from transition waiting probabilities for a Markov Regime Switching process, and from the tail index of the waiting time distribution for the Semi-Markov process is established through simulations with different parameter values. There is evidence that persistence in a regime switching process is closely linked to state transition probabilities and waiting times. The second chapter re-examines what structural vector autoregressive modeling of real exchange rates with differenced variables tells us about interesting macroeconomic questions. Using quarterly data from G-7 countries in the post Bretton-Woods period, the evidence suggests that shock identification is not an easy process in a Blanchard and Quah decomposition framework with long run restrictions. Confidence bands do not find significant impulse responses and the signs of the estimated impulse responses are very sensitive to the lag selection criteria adopted. Possible cointegration effects seem to be the main driving force behind the unsatisfactory performance of the structural approach. Chapter three extends the structural vector autoregression model by incorporating cointegration effects. Using the method of Warne (1993), in a simple four-variable vector autoregression (VAR) characterized by cointegration, the response of real exchange rates to various economic shocks are investigated with economically plausible long-run restrictions. The long-run relations and driving stochastic trends of the real exchange rate between United States and other G-7 countries are analyzed in a structural cointegrated framework. Productivity shocks depreciate the real exchange rate and the perverse sign effect of supply shock is corrected for most countries in the sample. More significant impulse responses are observed through confidence intervals. The structural vector error correction decompositions are also found to be not robust to estimating with different lag lengths owing to additional cointegration effects.
Publisher:
ISBN:
Category :
Languages : en
Pages : 218
Book Description
In the first chapter the performance of two of the long memory tests, the Modified Rescaled Range Test and Geweke and Porter-Hudak Test for persistence in small samples is examined using Monte-Carlo methods. Some possible candidates for persistence in volatility are Autoregressive Conditional Heteroskedasticity (ARCH), Markov Regime Switching ARCH, and long memory. The long memory series are simulated through a Semi-Markov process with Pareto waiting times and lognormal realizations. The persistence in volatility arising from transition waiting probabilities for a Markov Regime Switching process, and from the tail index of the waiting time distribution for the Semi-Markov process is established through simulations with different parameter values. There is evidence that persistence in a regime switching process is closely linked to state transition probabilities and waiting times. The second chapter re-examines what structural vector autoregressive modeling of real exchange rates with differenced variables tells us about interesting macroeconomic questions. Using quarterly data from G-7 countries in the post Bretton-Woods period, the evidence suggests that shock identification is not an easy process in a Blanchard and Quah decomposition framework with long run restrictions. Confidence bands do not find significant impulse responses and the signs of the estimated impulse responses are very sensitive to the lag selection criteria adopted. Possible cointegration effects seem to be the main driving force behind the unsatisfactory performance of the structural approach. Chapter three extends the structural vector autoregression model by incorporating cointegration effects. Using the method of Warne (1993), in a simple four-variable vector autoregression (VAR) characterized by cointegration, the response of real exchange rates to various economic shocks are investigated with economically plausible long-run restrictions. The long-run relations and driving stochastic trends of the real exchange rate between United States and other G-7 countries are analyzed in a structural cointegrated framework. Productivity shocks depreciate the real exchange rate and the perverse sign effect of supply shock is corrected for most countries in the sample. More significant impulse responses are observed through confidence intervals. The structural vector error correction decompositions are also found to be not robust to estimating with different lag lengths owing to additional cointegration effects.
Identification of Structural Shocks in Structural Vector
Author: Tim Kovalenko
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description