Essays On Empirical Time Series Modeling With Causality And Structural Change
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| Author | : Jin Woong Kim |
| Publisher | : |
| Total Pages | : |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
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.
| Author | : Judea Pearl |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 0 |
| Release | : 2015 |
| Genre | : Causation |
| ISBN | : 9781507894293 |
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
| Author | : Judea Pearl |
| Publisher | : Basic Books |
| Total Pages | : 432 |
| Release | : 2018-05-15 |
| Genre | : Computers |
| ISBN | : 0465097618 |
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
| Author | : Yan Liu |
| Publisher | : Springer Nature |
| Total Pages | : 591 |
| Release | : 2023-05-31 |
| Genre | : Mathematics |
| ISBN | : 9819908035 |
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.
| Author | : Tatevik Zohrabyan |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
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.
| Author | : |
| Publisher | : |
| Total Pages | : 700 |
| Release | : 2008 |
| Genre | : Dissertations, Academic |
| ISBN | : |
| Author | : James M. McCracken |
| Publisher | : Springer Nature |
| Total Pages | : 133 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031019091 |
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.
| Author | : Clive W. J. Granger |
| Publisher | : Cambridge University Press |
| Total Pages | : 400 |
| Release | : 2001-07-23 |
| Genre | : Business & Economics |
| ISBN | : 9780521796491 |
These are econometrician Clive W. J. Granger's major essays in causality, integration, cointegration, and long memory.
| Author | : Dek Terrell |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 418 |
| Release | : 2020-04-15 |
| Genre | : Business & Economics |
| ISBN | : 1789739594 |
Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
| Author | : Yanlin Shi |
| Publisher | : |
| Total Pages | : 326 |
| Release | : 2014 |
| Genre | : Time-series analysis |
| ISBN | : |
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.
