Essays On Empirical Time Series Modeling With Causality And Structural Change
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Essays On Empirical Time Series Modeling With Causality And Structural Change
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Author : Jin Woong Kim
language : en
Publisher:
Release Date : 2006
Essays On Empirical Time Series Modeling With Causality And Structural Change written by Jin Woong Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Causation categories.
In this dissertation, three related issues of building empirical time series models for financial markets are investigated with respect to contemporaneous causality, dynamics, and structural change. In the first essay, nation-wide industry information transmission among stock returns of ten sectors in the U.S. economy is examined through the Directed Acyclical Graph (DAG) for contemporaneous causality and Bernanke decomposition for dynamics. The evidence shows that the information technology sector is the most root cause sector. Test results show that DAG from ex ante forecast innovations is consistent with the DAG from ex post fit innovations. This supports innovation accounting based on DAGs using ex post innovations. In the second essay, the contemporaneous/dynamic behaviors of real estate and stock returns are investigated. Selected macroeconomic variables are included in the model to explain recent movements of both returns. During 1971-2004, there was a single structural break in October 1980. A distinct difference in contemporaneous causal structure before and after the break is found. DAG results show that REITs take the role of a causal parent after the break. Innovation accounting shows significantly positive responses of real estate returns due to an initial shock in default risk but insignificant responses of stock returns. Also, a shock in short run interest rates affects real estate returns negatively with significance but does not affect stock returns. In the third essay, a structural change in the volatility of five Asian and U.S. stockmarkets is examined during the post-liberalization period (1990-2005) in the Asian financial markets, using the Sup LM test. Four Asian financial markets (Hong Kong,Japan, Korea, and Singapore) experienced structural changes. However, test results do not support the existence of structural change in volatility for Thailand and U.S. Also, results show that the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) persistent coefficient increases, but the Autoregressive Conditional heteroskedasticity (ARCH) impact coefficient, implying short run adjustment, decreases in Asian markets. In conclusion, when the econometric model is set up, it is necessary to consider contemporaneous causality and possible structural breaks (changes). The dissertation emphasizes causal inference and structural consistency in econometric modeling. It highlights their importance in discovering contemporaneous/dynamic causal relationships among variables. These characteristics will likely be helpful in generating accurate forecasts.
Three Essays On Price Dynamics And Causations Among Energy Markets And Macroeconomic Information
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Author : Sung Wook Hong
language : en
Publisher:
Release Date : 2013
Three Essays On Price Dynamics And Causations Among Energy Markets And Macroeconomic Information written by Sung Wook Hong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148354
Exploratory Causal Analysis With Time Series Data
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Author : James M. McCracken
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Exploratory Causal Analysis With Time Series Data written by James M. McCracken and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.
Research Papers In Statistical Inference For Time Series And Related Models
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Author : Yan Liu
language : en
Publisher: Springer Nature
Release Date : 2023-05-31
Research Papers In Statistical Inference For Time Series And Related Models written by Yan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Mathematics categories.
This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Essays On Time Series Analysis
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Author : Yanlin Shi
language : en
Publisher:
Release Date : 2014
Essays On Time Series Analysis written by Yanlin Shi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Time-series analysis categories.
This thesis is a collection of essays on modelling volatility with time series techniques. The first essay addresses the question of modelling structural breaks in the Fractionally Integrated Generalised Autoregressive Conditional Heteroskedasticity (FIGARCH) model. By detecting structural change points via the Markov Regime-Switching (MRS) framework, a two-stage Three-State FIGARCH (3S-FIGARCH) model is proposed. Compared with various existing FIGARCH family models, our empirical results suggest that the 3S-FIGARCH model is preferred in all cases and can potentially provide a more reliable estimate of the long-memory parameter. The second essay examines the confusion between long memory and regime switching in volatility via a set of Monte Carlo simulations. A theoretical proof is provided to show that this confusion is caused by the effects of the smoothing probability from the data-generating process (DGP) of the MRS-GARCH model. To control for these effects, the MRS-FIGARCH model is proposed. By conducting a set of Monte Carlo simulations, we show that the MRS-FIGARCH model can effectively distinguish between the pure FIGARCH and pure MRS-GARCH DGPs. Further, an empirical application suggests that the MRS-FIGARCH can be a widely useful tool for volatility modelling. The third essay empirically studies the relation between public information arrivals and intraday stock return volatility. Motivated by the Mixture of Distribution Hypothesis (MDH) and the study of Veronesi (1999), we fit hourly Standard & Poor's (S&P) 100 stock return data with the MRS-GARCH model to investigate the effect of the quantity and quality of news on stock return volatility in the calm (low volatility) and turbulent (high volatility) states. The effect of news on the persistence and magnitude of volatility depends on the quality of news and the state of stock return volatility. In addition, this effect varies across sectors and firm sizes. The fourth essay analyses the effects of news on the so-called 'idiosyncratic volatility puzzle'. By empirically modelling the stock return data from the Center for Research in Security Prices (CRSP) database from 2000 to 2011, we demonstrate that both the quantity and quality of news can significantly explain the effect of idiosyncratic volatility on excess returns. Specifically, when news effects are appropriately controlled, the average magnitude of this effect can be reduced by roughly 50 per cent.
Essays On Time Series And Causality Analysis In Financial Markets
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Author : Tatevik Zohrabyan
language : en
Publisher:
Release Date : 2010
Essays On Time Series And Causality Analysis In Financial Markets written by Tatevik Zohrabyan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Financial market and its various components are currently in turmoil. Many large corporations are devising new ways to overcome the current market instability. Consequently, any study fostering the understanding of financial markets and the dependencies of various market components would greatly benefit both the practitioners and academicians. To understand different parts of the financial market, this dissertation employs time series methods to model causality and structure and degree of dependence. The relationship of housing market prices for nine U.S. census divisions is studied in the first essay. The results show that housing market is very interrelated. The New England and West North Central census divisions strongly lead house prices of the rest of the country. Further evidence suggests that house prices of most census divisions are mainly influenced by house price changes of other regions. The interdependence of oil prices and stock market indices across countries is examined in the second essay. The general dependence structure and degree is estimated using copula functions. The findings show weak dependence between stock market indices and oil prices for most countries except for the large oil producing nations which show high dependence. The dependence structure for most oil consuming (producing) countries is asymmetric implying that stock market index and oil price returns tend to move together more during the market downturn (upturn) than a market boom (downturn). In the third essay, the relationship among stock returns of ten U.S. sectors is studied. Copula models are used to explore the non-linear, general association among the series. The evidence shows that sectors are strongly related to each other. Energy sector is relatively weakly connected with the other sectors. The strongest dependence is between the Industrials and Consumer Discretionary sectors. The high dependence suggests small (if any) gains from industry diversification in U.S. In conclusion, the correct formulation of relationships among variables of interest is crucial. This is one of the fundamental issues in portfolio analysis. Hence, a thorough examination of time series models that are used to understand interactions of financial markets can be helpful for devising more accurate investment strategies.
Time Series Analysis And Adjustment
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Author : Haim Y. Bleikh
language : en
Publisher: CRC Press
Release Date : 2016-02-24
Time Series Analysis And Adjustment written by Haim Y. Bleikh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-24 with Business & Economics categories.
In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.
Introduction To Modern Time Series Analysis
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Author : Gebhard Kirchgässner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-09
Introduction To Modern Time Series Analysis written by Gebhard Kirchgässner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-09 with Business & Economics categories.
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
Essays In Time Series Econometrics
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Author : Neslihan Sakarya
language : en
Publisher:
Release Date : 2017
Essays In Time Series Econometrics written by Neslihan Sakarya and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
This dissertation consists of four research papers. Three of these papers are based on the analysis the Hodrick-Prescott (HP) filter which is a commonly used technique to extract the trend from a time series in macroeconomics, while the last paper introduces a monitoring procedure to detect a change from spurious regression to cointegration. In the first paper, we derive a new representation of the transformation of the data that is implied by the HP filter. This representation allows us to carry out a rigorous analysis of the properties of the HP filter without using the ARMA based approximation that has been used in the previous literature. In the second paper, we introduce a new property of the HP filter that has not been discovered before. When the trend is extracted from the original time series, the remaining series is called the cyclical component. The new property suggests that the cyclical component is approximately the trend in the fourth difference of the original series. We formalize this approximation by correcting it for the begin and end points of the sample. This property allows us to analyze the properties of the cyclical component when the original series has a linear trend break or is integrated of order up to 4. The third paper approaches the HP filter from frequency domain approach, unlike the first two papers. Since the results of the previous literature are based on the spectral properties of a procedure that is only an approximation to the HP filter, in this paper, we formalize the conjectures that are provided in the literature. The last paper introduces a monitoring procedure to detect a structural break that changes the relation between two integrated time series. It is well-known that two integrated series are highly correlated, while the causality between these series is not obvious. Cointegration is a term that describes the existence of causality between two integrated series. The null hypothesis of the monitoring procedure is that the regression is spurious throughout the sample, whereas under the alternative hypothesis, there is a change from spurious regression to cointegration at an unknown breakpoint. We derive the limiting distribution of the detector under both the null and (fixed and local) alternative hypotheses. The monitoring procedure is consistent, as long as the structural break is bounded away from the end of the sample. Simulation results suggest that the procedure has excellent size properties, as well as good power properties. Finally, the monitoring procedure is applied to the relationship between US nominal wages and prices; a relation found not to exhibit cointegration in the seminal Engle and Granger (1987) paper. Our monitoring procedure indicates a change towards cointegration around the beginning of the new millennium.
Extracting Knowledge From Time Series
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Author : Boris P. Bezruchko
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-03
Extracting Knowledge From Time Series written by Boris P. Bezruchko and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-03 with Science categories.
Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.