Download or read book Essays on the Volatility of Macroeconomic and Financial Time Series written by Wei-Choun Yu and published by . This book was released on 2006 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility and Time Series Econometrics written by Tim Bollerslev and published by OUP Oxford. This book was released on 2010-02-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.

Download or read book Volatility and Time Series Econometrics written by Mark Watson and published by Oxford University Press. This book was released on 2010-02-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A volume that celebrates and develops the work of Nobel Laureate Robert Engle, it includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics

Download or read book Essays on Time Series Forecasting with Neural network Or Long dependence Autoregressive Models and Macroeconomic News Effects on Bond Yields written by Morvan Nongni Donfack and published by . This book was released on 2022 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis, organized in three chapters, focuses on modelling and forecasting economic and financial time series. The first two chapters propose new econometric models for analysing economic and financial data by relaxing unrealistic assumptions usually made in the literature. Chapter 1 develops a new volatility model named TVP[subscript ANN]-GARCH. The model offers rich dynamics to model financial data by allowing for a generalized autoregressive conditional heteroscedasticity (GARCH) structure in which parameters vary over time according to an artificial neural network (ANN). The use of ANNs for parameters dynamics is a valuable contribution as it helps to deal with the problem of likelihood evaluation (exhibited in time-varying parameters (TVP) models). It also allows for the use of additional explanatory variables. The chapter develops an original and efficient Sequential Monte Carlo sampler (SMC) to estimate the model. An empirical application shows that the model favourably compares to popular volatility processes in terms of out-of sample fit. The approach can easily be extended to any fixed-parameters model. Chapter 2 develops three parsimonious autoregressive (AR) lag polynomials that generate slowly decaying autocorrelation functions as generally observed financial and economic time series. The dynamics of the lag polynomials are similar to that of two well performing processes, namely the Markov-Switching Multifractal (MSM) and the Factorial Hidden Markov Volatility (FHMV) models. They are very flexible as they can be applied in many popular models such as ARMA, GARCH, and stochastic volatility processes. An empirical analysis highlights the usefulness of the lag polynomials for conditional mean and volatility forecasting. They could be considered as forecasting alternatives for economic and financial time series. The last chapter relies on a two steps predictive regression approach to identify the impact of US macroeconomic releases on three small open economies (Canada, United Kingdom, and Sweden) bond yields at high and low frequencies. Our findings suggest that US macro news are significantly more important in explaining yield curve dynamics in small open economies (SOEs) than domestic news itself. Not only US monetary policy news are important drivers of SOEs bond yield changes, but business cycle news also play a significant role.

Download or read book Essays on Time Varying Volatility and Structural Breaks in Macroeconomics and Econometrics written by Nyamekye Asare and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is comprised of three independent essays. One essay is in the field of macroeconomics and the other two are in time-series econometrics. The first essay, "Productivity and Business Investment over the Business Cycle", is co-authored with my co-supervisor Hashmat Khan. This essay documents a new stylized fact: the correlation between labour productivity and real business investment in the U.S. data switching from 0.54 to -0.1 in 1990. With the assistance of a bivariate VAR, we find that the response of investment to identified technology shocks has changed signs from positive to negative across two sub-periods: ranging from the time of the post-WWII era to the end of 1980s and from 1990 onwards, whereas the response to non-technology shocks has remained relatively unchanged. Also, the volatility of technology shocks declined less relative to the non-technology shocks. This raises the question of whether relatively more volatile technology shocks and the negative response of investment can together account for the decreased correlation. To answer this question, we consider a canonical DSGE model and simulate data under a variety of assumptions about the parameters representing structural features and volatility of shocks. The second and third essays are in time series econometrics and solely authored by myself. The second essay, however, focuses on the impact of ignoring structural breaks in the conditional volatility parameters on time-varying volatility parameters. The focal point of the third essay is on empirical relevance of structural breaks in time-varying volatility models and the forecasting gains of accommodating structural breaks in the unconditional variance. There are several ways in modeling time-varying volatility. One way is to use the autoregressive conditional heteroskedasticity (ARCH)/generalized ARCH (GARCH) class first introduced by Engle (1982) and Bollerslev (1986). One prominent model is Bollerslev (1986) GARCH model in which the conditional volatility is updated by its own residuals and its lags. This class of models is popular amongst practitioners in finance because they are able to capture stylized facts about asset returns such as fat tails and volatility clustering (Engle and Patton, 2001; Zivot, 2009) and require maximum likelihood methods for estimation. They also perform well in forecasting volatility. For example, Hansen and Lunde (2005) find that it is difficult to beat a simple GARCH(1,1) model in forecasting exchange rate volatility. Another way of modeling time-varying volatility is to use the class of stochastic volatility (SV) models including Taylor's (1986) autoregressive stochastic volatility (ARSV) model. With SV models, the conditional volatility is updated only by its own lags and increasingly used in macroeconomic modeling (i.e.Justiniano and Primiceri (2010)). Fernandez-Villaverde and Rubio-Ramirez (2010) claim that the stochastic volatility model fits better than the GARCH model and is easier to incorporate into DSGE models. However, Creal et al. (2013) recently introduced a new class of models called the generalized autoregressive score (GAS) models. With the GAS volatility framework, the conditional variance is updated by the scaled score of the model's density function instead of the squared residuals. According to Creal et al. (2013), GAS models are advantageous to use because updating the conditional variance using the score of the log-density instead of the second moments can improve a model's fit to data. They are also found to be less sensitive to other forms of misspecification such as outliers. As mentioned by Maddala and Kim (1998), structural breaks are considered to be one form of outliers. This raises the question about whether GAS volatility models are less sensitive to parameter non-constancy. This issue of ignoring structural breaks in the volatility parameters is important because neglecting breaks can cause the conditional variance to exhibit unit root behaviour in which the unconditional variance is undefined, implying that any shock to the variance will not gradually decline (Lamoureux and Lastrapes, 1990). The impact of ignoring parameter non-constancy is found in GARCH literature (see Lamoureux and Lastrapes, 1990; Hillebrand, 2005) and in SV literature (Psaradakis and Tzavalis, 1999; Kramer and Messow, 2012) in which the estimated persistence parameter overestimates its true value and approaches one. However, it has never been addressed in GAS literature until now. The second essay uses a simple Monte-Carlo simulation study to examine the impact of neglecting parameter non-constancy on the estimated persistence parameter of several GAS and non-GAS models of volatility. Five different volatility models are examined. Of these models, three --the GARCH(1,1), t-GAS(1,1), and Beta-t-EGARCH(1,1) models -- are GAS models, while the other two -- the t-GARCH(1,1) and EGARCH(1,1) models -- are not. Following Hillebrand (2005) who studied only the GARCH model, this essay examines the extent of how biased the estimated persistence parameter are by assessing impact of ignoring breaks on the mean value of the estimated persistence parameter. The impact of neglecting parameter non-constancy on the empirical sampling distributions and coverage probabilities for the estimated persistence parameters are also studied in this essay. For the latter, studying the effect on the coverage probabilities is important because a decrease in coverage probabilities is associated with an increase in Type I error. This study has implications for forecasting. If the size of an ignored break in parameters is small, then there may not be any gains in using forecast methods that accommodate breaks. Empirical evidence suggests that structural breaks are present in data on macro-financial variables such as oil prices and exchange rates. The potentially serious consequences of ignoring a break in GARCH parameters motivated Rapach and Strauss (2008) and Arouri et al. (2012) to study the empirical relevance of structural breaks in the context of GARCH models. However, the literature does not address the empirical relevance of structural breaks in the context of GAS models. The third and final essay contributes to this literature by extending Rapach and Strauss (2008) to include the t-GAS model and by comparing its performance to that of two non-GAS models, the t-GARCH and SV models. The empirical relevance of structural breaks in the models of volatility is assessed using a formal test by Dufour and Torres (1998) to determine how much the estimated parameters change over sub-periods. The in-sample performance of all the models is analyzed using both the weekly USD trade-weighted index between January 1973 and October 2016 and spot oil prices based on West Texas Intermediate between January 1986 and October 2016. The full sample is split into smaller subsamples by break dates chosen based on historical events and policy changes rather than formal tests. This is because commonly-used tests such as CUSUM suffer from low power (Smith, 2008; Xu, 2013). For each sub-period, all models are estimated using either oil or USD returns. The confidence intervals are constructed for the constant of the conditional parameter and the score parameter (or ARCH parameter in GARCH and t-GARCH models). Then Dufour and Torres's union-intersection test is applied to these confidence intervals to determine how much the estimated parameter change over sub-periods. If there is a set of values that intersects the confidence intervals of all sub-periods, then one can conclude that the parameters do not change that much. The out-of-sample performance of all time-varying volatility models are also assessed in the ability to forecast the mean and variance of oil and USD returns. Through this analysis, this essay also addresses whether using models that accommodate structural breaks in the unconditional variance of both GAS and non-GAS models will improve forecasts.

Download or read book Nonlinear Economic Dynamics and Financial Modelling written by Roberto Dieci and published by Springer. This book was released on 2014-07-26 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reflects the state of the art on nonlinear economic dynamics, financial market modelling and quantitative finance. It contains eighteen papers with topics ranging from disequilibrium macroeconomics, monetary dynamics, monopoly, financial market and limit order market models with boundedly rational heterogeneous agents to estimation, time series modelling and empirical analysis and from risk management of interest-rate products, futures price volatility and American option pricing with stochastic volatility to evaluation of risk and derivatives of electricity market. The book illustrates some of the most recent research tools in these areas and will be of interest to economists working in economic dynamics and financial market modelling, to mathematicians who are interested in applying complexity theory to economics and finance and to market practitioners and researchers in quantitative finance interested in limit order, futures and electricity market modelling, derivative pricing and risk management.

Download or read book Essays in Economic and Financial Time Series Analysis written by Fotis Papailias and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The research presented in this thesis contributes to four areas in the Economic and Financial Time Series Analysis literature. These include the topics of (i) Selection of Long Memory Time Series Models, (ii) Bootstrapping Strongly Dependent Data, (iii) Forecasting Key Macroeconomic Variables and (iv) Portfolio Optimisation. The first part focuses on strongly dependent series. It aims to establish an asymptotically consistent information criterion for long memory processes when the long memory parameter is semi parametrically estimated. A set of Monte Carlo experiments and the analysis of monthly inflation time series show the validity of the new methodology. Next, we are concerned with the issue of bootstrap in strongly dependent data. We introduce a fractional differencing bootstrap methodology that allows the implementation of any resampling method in such series. Evidence of robustness is given by Monte Carlo experiments using various block and residuals resampling schemes. The second part of the thesis investigates the issue of forecasting macroeconomic variables. Heuristic methods for the optimisation of information criteria are employed and their forecasting performance is compared to the standard choices in the literature. The empirical application in Euro Area dataset suggests that the non-standard methods should be taken into consideration as they provide better forecasts on average. The last part of the thesis investigates the applied performance of covariance shrinkage in the portfolio optimisation problem when the universe of assets is large. Our approach suggests the use of a shrinkage coefficient that optimises functions with financial interpretation. Empirical results provide evidence that the shrinkage portfolios obtained using the suggested approach are characterised by higher Sharpe Ratios, cumulative returns and profit/loss ratio.

Download or read book Essays in Macro Finance and Monetary Economics written by Modeste Yirbèhogré Somé and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Macro finance Relationships written by Azamat Abdymomunov and published by . This book was released on 2010 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: In my dissertation, I study relationships between macroeconomics and financial markets. In particular, I empirically investigate the links between key macroeconomic indicators, such as output, inflation, and the business cycle, and the pricing of financial assets. The dissertation comprises three essays. The first essay investigates how the entire term structure of interest rates is influenced by regime-shifts in monetary policy. To do so, we develop and estimate an arbitrage-free dynamic term-structure model which accounts for regime shifts in monetary policy, volatility, and the price of risk. Our results for U.S. data from 1985-2008 indicate that (i) the Fed's reaction to inflation has changed over time, switching between "more active" and "less active" monetary policy regimes, (ii) the yield curve in the "more active" regime was considerably more volatile than in the "less active" regime, and (iii) on average, the slope of the yield curve in the "more active" regime was steeper than in the "less active" regime. The steeper yield curve in the "more active" regime reflects higher term premia that result from the risk associated with a more volatile future short-term rate given a more sensitive response to inflation. The second essay examines the predictive power of the entire yield curve for aggregate output. Many studies find that yields for government bonds predict real economic activity. Most of these studies use the yield spread, defined as the difference between two yields of specific maturities, to predict output. In this paper, I propose a different approach that makes use of information contained in the entire term structure of U.S. Treasury yields to predict U.S. real GDP growth. My proposed dynamic yield curve model produces better out-of-sample forecasts of real GDP than those produced by the traditional yield spread model. The main source of this improvement is in the dynamic approach to constructing forecasts versus the direct forecasting approach used in the traditional yield spread model. Although the predictive power of yield curve for output is concentrated in the yield spread, there is also a gain from using information in the curvature factor for the real GDP growth prediction. The third essay investigates time variation in CAPM betas for book-to-market and momentum portfolios across stock market volatility regimes. For our analysis, we jointly model market and portfolio returns using a two-state Markov-switching process, with beta and the market risk premium allowed to vary between "low" and "high" volatility regimes. Our empirical findings suggest strong time variation in betas across volatility regimes in most of the cases for which the unconditional CAPM can be rejected. Although the regime-switching conditional CAPM can still be rejected in many cases, the time-varying betas help explain portfolio returns much better than the unconditional CAPM, especially when market volatility is high.

Download or read book Essays on Financial Time Series Volatility written by Jun Cai and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Macroeconomic Volatility and the Great Moderation written by Michael W. Clark and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is a collection of two essays on the macroeconomic volatility and the Great Moderation. The first essay examines the causes of the Great Moderation in United States, while the second essay takes an international approach in examining if the Great Moderation was one or multiple events for the industrialized countries. The first essay analyzes the causes of the large decline in aggregate volatility for the United States, phenomenon known as the Great Moderation, one of the most widely recognized characteristics of the modern U.S. economy. However, the literature found no consensus on what caused it. In order to uncover the causes of the Great Moderation we use a new measure of volatility based on the first difference of quarterly growth rates, and a novel approach, exploiting a test for common features. We first test each series for structural change(s) in volatility, and then test for a common feature of a decrease in volatility between the volatility of output and volatility of potential causes of the Great Moderation for both the period prior to the Great Recession (2007:4) and the whole sample through 2010:4. When all the evidence is considered, structural changes in the economy, including increased globalization and improved inventory management, improved monetary policy, and good luck, all appear to have played a significant role, while financial market innovations are unlikely to be a cause of the Great Moderation. The second essay analyzes if the Great Moderation is one event internationally, common across countries, or multiple events. The Great Moderation has been identified in several advanced economies as a general decrease in the volatility of GDP growth, and it is still viewed as one time event. We use structural break test to date the onset of the Great Moderation in eleven developed countries and employ the test for common features in order to determine if the moderation in volatility is common across countries (one event), or if it is more than one event. While we establish that all of the countries studied display a break dating from the late 1970s to mid- 1980s and early 1990s, we discover the moderation of volatility evident in international data is neither concurrent, nor of similar magnitude. We can use this new information to enlighten our search for the cause(s) of the Great Moderation by both eliminating potential causes and increasing the ability to distinguish between causality and coincidence.

Download or read book Dynamic Modeling Empirical Macroeconomics and Finance written by Lucas Bernard and published by Springer. This book was released on 2016-10-03 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume, with contributions by area experts, offers discussions on a range of evolving topics in economics and social development. At center are important issues central to sustainable development, economic growth, technological change, the economics of climate change, commodity markets, long wave theory, non-linear dynamic models, and boom-bust cycles. This is an excellent reference for academic and professional economists interested in emerging areas of empirical macroeconomics and finance. For policy makers and curious readers alike, it is also an outstanding introduction to the economic thinking of those who seek a holistic and all-compassing approach in economic theory and policy. Looking into new data and methodology, this book offers fresh approaches in a post-crisis environment. Set in a profound understanding of the diverse currents within the many traditions of economic thought, this book pushes the established frontiers of economic thinking. It is dedicated to a leading scholar in the areas covered in this book, Willi Semmler.

Download or read book Essays on Macroeconomics and Financial Stability written by Pablo Garcia Sanchez and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2008 crisis and the ensuing Great Recession shook the consensus on how to run economic policy. They reminded us that financial imbalances could significantly derail economic activity. In addition, they showed that existing policy tools did not guarantee macro-financial stability; thereby leading to a rethink of monetary policy and financial regulation. Such a reevaluation has prompted a call for macroprudential tools, i.e., those tools intended for limiting systemic risk and ensuring the resilience of the financial sector. Besides, it has raised new questions about monetary policy and its effects on the risk taking behavior of economic agents - the so-called risk taking channel. A decade from the beginning of the crisis, the contours of a new policy framework for economic and financial stability are still very unclear. Knowledge on which regulatory instruments and how to employ them to curb the buildup of imbalances is limited. Neither is much known about the costs of those instruments.Regulatory intervention constraints some behaviors and distorts the allocation of resources. Consequently, the risk of imposing insidious costs on economic growth must not be underestimated. Likewise, very little is known about the relationship between monetary policy and the perception and pricing of risk by market participants. Nonetheless, it is natural to think that the monetary policy stance may affect the risk taking behavior of economic units, by influencing the attitudes towards risk and the assessment of risks. If so, failure by monetary authorities to consider this phenomenon could exacerbate boom bust patterns. The aim of this thesis is to explore the path towards macroeconomic and financial stability. I have basedmy work on the modern dynamic macroeconomic methods and techniques. Specifically, the first essay develops a canonical real business cycle model to assess the macroeconomic consequences of bank capital requirements, arguably the most used prudential tool. The second essay zooms in on the banking sector, and proposes a structural dynamic model with a large number of heterogeneous banks. The model is employed to study the effectiveness of interbank exposure limits. Having analyzed regulatory intervention, the last essay uses time series econometrics to shed some light on the risk taking channel of monetary policy. It is my firm belief that macroeconomics models for financial stability analysis should consider nonlinear patterns such as state dependence, asymmetries and amplification effects. Under unusual conditions like financial booms or credit crunches, economic agents behave differently than during normal times. In other words, the inner workings of the macroeconomy become essentially nonlinear under abnormal circumstances. Therefore, local behavior around the long run equilibrium of the economy is unlikely to contain relevant information about what may happen in exceptional events. In consequence, I study macroeconomic policy exclusively through the lens of nonlinear frameworks and techniques. Regarding the main results, this thesis makes a strong case in favor of macroprudential regulation. I provide clear evidence suggesting that regulatory intervention can be a powerful tool to strengthen financial resilience, reduce economic volatility and smooth business cycles. In addition, this thesis shows that accommodative monetary policy can produce overconfidence among market participants; thereby increasing risk taking and contributing to the buildup of imbalances. In other words, it provides empirical evidence for the existence of a risk taking channel of monetary policy.

Download or read book Essays on Time Series Models with Dynamic Coefficients in Macroeconomics and Finance written by Yunmi Kim and published by . This book was released on 2008 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Forecasting Financial and Economic Time Series written by Mohaimen Mansur and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Modern Time Series Econometrics with Applications in Macroeconomics and Finance written by Julie Schnaitmann and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series Analysis and Macroeconometric Modelling written by Kenneth Frank Wallis and published by Edward Elgar Publishing. This book was released on 1995 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A collection of 28 essays by Wallis (econometrics, U. of Warwick, UK), published from 1966 to 1991, on the statistical analysis of economic time series, large-scale macroeconometric modeling, and the interface between them. The articles are organized in four parts: time-series econometrics; modeling seasonality; forecasting in theory and practice; and macroeconometric modeling. The introduction by Wallis provides the background to the papers and comments on subsequent developments. Indexed by name only. Distributed by Ashgate. Annotation copyright by Book News, Inc., Portland, OR