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Essays In Garch And Regime Switching Models
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Book Synopsis Essays in GARCH and Regime Switching Models by : André Oliveira Santos
Download or read book Essays in GARCH and Regime Switching Models written by André Oliveira Santos and published by . This book was released on 1999 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses applications of GARCH and regime switching models to financial markets and contains four chapters. The second chapter allows different parameters in the GARCH process for different situations of volatility in financial markets. The data generating process for asset returns has a second moment that is time-varying, persistent and subject to suddent regime shifts. The third chapter identifies how regime-dependent stochastic trends in fundamentals affect the behavior of exchange rates given an exchange rate determination model. Big swings in exchange rates in the chapter are the result of stochastic trends in fundamentals if exchange rates are an endogenous variable in the economy. Finally, the fourth chapter tests the forward-looking rational expectations monetary model of exchange rate determination with present value models, in a VAR context and when the data generation process is subject to changes in regime.
Book Synopsis Multivariate GARCH-in-mean and Regime Switching in Intertemporal and International Capital Asset Pricing Models by : Lorenzo Cappiello
Download or read book Multivariate GARCH-in-mean and Regime Switching in Intertemporal and International Capital Asset Pricing Models written by Lorenzo Cappiello and published by . This book was released on 2001 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Time Series Analysis by : Yanlin Shi
Download or read book Essays on Time Series Analysis written by Yanlin Shi and published by . This book was released on 2014 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Essays on Financial Return and Volatility Modeling by : Jing Wu (Ph. D.)
Download or read book Essays on Financial Return and Volatility Modeling written by Jing Wu (Ph. D.) and published by . This book was released on 2012 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three essays focusing on modeling financial asset return and volatility. The first essay proposes a threshold GARCH model to describe the regimeswitching in volatility dynamics of financial asset returns. In the threshold model the switching of regimes is triggered by an observable threshold variable, while volatility follows a GARCH process within each regime. This model can be viewed as a special case of the random coefficient GARCH model. We establish theoretical conditions, which ensure that the return process in the threshold model is strictly stationary, as well as conditions for the existence of finite variance and fourth moment. A simulation study is further conducted to examine the finite sample properties of the maximum likelihood estimator. The second essay extends our study of the threshold GARCH model to an empirical application. The empirical results support the use of the threshold variable to identify the regime shifts in the volatility process. Especially when VIX is used as the threshold, we are able to separate the clustering of volatile periods corresponding to various financial crises. According to 5 common measures on forecasting evaluation, the threshold GARCH model provides better volatility forecasts for stocks as well as currency exchange rates. The third essay examines the effect of time structure on the estimation performance of independent component analysis (ICA) models and provides guidance in applying the ICA model to time series data. We compare the performance of the basic ICA model to the time series ICA model in which the cross-autocovariances are used as a measure to achieve independence. We conduct a simulation study to evaluate the time series ICA model under different time structure assumptions about the underlying components that generate financial time series. Moreover, the empirical results support the use of the time series ICA model.
Book Synopsis Volatility, Duration, and Value-at-risk by : Pujin Liu
Download or read book Volatility, Duration, and Value-at-risk written by Pujin Liu and published by . This book was released on 2012 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis consists of three essays dealing with the modeling of volatility in financial markets, trade durations, and Value-at-Risk (VaR). The first essay models nonlinearities in the return series to estimate time-varying volatility by incorporating both regime changes and jumps. Two types of regime-switching GARCH-jump models with autoregressive jump intensity are presented. The first model follows the traditional Markov regime-switching model proposed in Hamilton (1989). As the unknown regimes in the Markov model lead to difficulty in forecasting, a threshold GARCH-jump model, in which regimes are known after observing the threshold variable in the previous period, is also proposed. The second essay models the intraday durations between two adjacent trade transactions by considering the impact of unaccounted struc- tural changes on parameter estimates. Monte Carlo simulations show that the observed high persistence in trade durations can be spurious and caused by unaccounted structural changes in the data generating process. The third essay investigates the use of realized moments in VaR forecasting, which is an important issue in risk management. Many VaR models rely only on the mean and volatility and ignore higher moments of returns, which leads to un- derestimation of VaR due to the unaccounted fat-tail property of the return series. Applying the Cornish-Fisher expansion to incorporate realized higher moments constructed from high frequency data, the proposed realized moment models outperform the realized volatility model and the traditional RiskMet- rics model, especially during the financial crisis period (2008-09).
Book Synopsis Three Essays on the Application of the Markov Switching Multifractal Model by : Waleem Babatunde Alausa
Download or read book Three Essays on the Application of the Markov Switching Multifractal Model written by Waleem Babatunde Alausa and published by . This book was released on 2014 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overall purpose of this thesis is to extend and apply the Markov Switching Multifractal (MSM) model to various economic problems. To this extent, Chapter 1 lays the ground work for the next chapters by reviewing the MSM model, discussing its properties and outlining its estimation procedures. The chapter also reviews the distributional properties of several commodity markets that make them amenable to the MSM model. Chapter 2 extends the MSM model by incorporating a vector error correction component, which includes in the conditional mean equation, the cointegrating relationship between spot and futures prices. The VECM-MSM model has two distinctive features that incorporate the empirical properties of asset prices. First, it includes an error correction mechanism in the mean equation that incorporates the long-run relationship between spot and futures prices. Second, the model specifies the conditional second moments as a bivariate Markov Switching Multifractal (MSM) model. The VECM-MSM model is applied to study the problem of risk hedging in the futures market. The hedging effectiveness of the proposed VECM-MSM model is evaluated, using a value-at-risk (VaR) approach. Specifically, we compare the hedging effectiveness of the proposed model to those of alternative models by assessing their unconditional and conditional VaR coverages. Models are then ranked in terms of the adequacy and accuracy of their hedged portfolio VaR. The in-sample and out-of-sample hedge effectiveness shows that the VECM-MSM hedged portfolio outperforms alternative hedging strategies in terms of having the lowest rate of VaR violations among the different strategies. Statistical tests of unconditional and conditional coverages also show that the VECM-MSM model better predicts an investor's downside risk in that the VaR predictions are more accurate than the predictions from the alternative models. Chapter 3 of this thesis investigates the excess commodity comovement phenomenon, using the MSM model. One of the stylized facts of commodity prices is their tendency for comovement. The phenomenon implies that seemingly unrelated commodities tend to move together beyond what can be attributed to fundamentals, such as demand and supply conditions, exchange rates, interest rates, industrial production etc. Excess commodity comovement bears significant welfare and risk management implications. For an instance, a synchronous rise in prices of commodities exerts significant inflationary pressure on commodity import dependent countries, and limits their ability to maintain economic stability and resist inflationary pressures. Moreover, to the extent that comovement measures, such as correlation and covariance among commodities, comprise an essential ingredient in risk assessment, pricing, portfolio management and hedging, failure to account for such excess comovement can lead to sub-optimal economic decisions. Therefore within the debate on excess commodity comovement, the objective of this chapter is twofold. First, it analyzes the degree of excess commodity comovement across a variety of commodities. Second, it analyzes the frequency-dependent nature of comovement across related (e.g. crude and heating oil) and unrelated commodities (e.g. copper and corn). First, we find that there is significant comovement between commodity prices, beyond what can simply be explained by macroeconomic fundamentals. Second, decomposing comovements into multiple frequencies, we find that all commodities exhibit long-run excess comovements which are driven by low frequency fundamentals such as weather, demographic and macroeconomic factors. But some commodities also exhibit significant short-run excess comovements that may be attributable to short-run factors such as liquidity constraints, indexation, etc. Third, the dynamic correlations show that excess comovements are higher in periods of high volatility and vice-versa. The final chapter applies a new class of model, the Autoregressive Markov switching multifractal model, for forecasting spot electricity prices. Three variants of the model are examinedEmploying hourly prices from the AESO market, the parameters of the ARX-MSM models are estimated, and one-step-ahead hourly forecasts are obtained. To put the performance of the ARX-MSM models into perspective, the results are compared to those of other notable models used in the literature, namely the AR(1), ARX, ARX-GARCH, mean-reverting jump and the 2-state independent Markov regime switching models. Goodness-of-fit tests indicate that the ARX-MSM models fit the data significantly better than the competing models. Likewise, out-of-sample results show that the ARX-MSM models provide better forecast accuracy.
Book Synopsis Essays on the Volatility of Macroeconomic and Financial Time Series by : Wei-Choun Yu
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:
Book Synopsis Essays on Financial Econometrics by : Juri Marcucci
Download or read book Essays on Financial Econometrics written by Juri Marcucci and published by . This book was released on 2005 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains three self-contained chapters dealing with volatility modeling and forecasting. In the first chapter we compare a set of standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the US stock market volatility at horizons that range from one day to one month. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at horizons shorter than one week. In particular, all tests reject the presence of a better model than the MRS-GARCH with normal innovations. However, at forecast horizons longer than one week, standard asymmetric GARCH models tend to be superior. In chapter 2 a new model to analyze the comovements in the volatilities of a portfolio is proposed. The Pure Variance Common Features model is a factor model for the conditional variances of a portfolio of assets, designed to isolate a small number of variance features that drive all assets' volatilities. It decomposes the conditional variance into a short-run idiosyncratic component (a low-order ARCH process) and a long-run component (the variance factors). An empirical example provides evidence that models with very few variance features perform well in capturing the long-run common volatilities of the equity components of the Dow Jones. In the third and last chapter we compare standard univariate models and multivariate factor models in terms of their ability to forecast the realized variances of a group of major international stock exchanges. Our results show that those models adopting equally weighted regional factors outperform all the others. In addition, models that use factors obtained from canonical correlation analysis tend to outperform all the others that employ different multivariate techniques, therefore confirming their predicting power.
Book Synopsis Essays in Nonlinear Time Series Econometrics by : Niels Haldrup
Download or read book Essays in Nonlinear Time Series Econometrics written by Niels Haldrup and published by OUP Oxford. This book was released on 2014-06-26 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.
Book Synopsis Essays on Financial Time Series by : Isao Ishida
Download or read book Essays on Financial Time Series written by Isao Ishida and published by . This book was released on 2004 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mathematical and Statistical Methods for Actuarial Sciences and Finance by : Marco Corazza
Download or read book Mathematical and Statistical Methods for Actuarial Sciences and Finance written by Marco Corazza and published by Springer. This book was released on 2017-12-28 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers selected peer-reviewed papers presented at the international conference "MAF 2016 – Mathematical and Statistical Methods for Actuarial Sciences and Finance”, held in Paris (France) at the Université Paris-Dauphine from March 30 to April 1, 2016. The contributions highlight new ideas on mathematical and statistical methods in actuarial sciences and finance. The cooperation between mathematicians and statisticians working in insurance and finance is a very fruitful field, one that yields unique theoretical models and practical applications, as well as new insights in the discussion of problems of national and international interest. This volume is addressed to academicians, researchers, Ph.D. students and professionals.
Book Synopsis Essays in Honor of Peter C. B. Phillips by : Thomas B. Fomby
Download or read book Essays in Honor of Peter C. B. Phillips written by Thomas B. Fomby and published by Emerald Group Publishing. This book was released on 2014-11-21 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume honors Professor Peter C.B. Phillips' many contributions to the field of econometrics. The topics include non-stationary time series, panel models, financial econometrics, predictive tests, IV estimation and inference, difference-in-difference regressions, stochastic dominance techniques, and information matrix testing.
Book Synopsis Three Essays on Monetary Economics by : Shiu-Sheng Chen
Download or read book Three Essays on Monetary Economics written by Shiu-Sheng Chen and published by . This book was released on 2004 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Time Series Forecasting with Neural-network Or Long-dependence Autoregressive Models and Macroeconomic News Effects on Bond Yields by : Morvan Nongni Donfack
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.
Book Synopsis Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration by : Greg N. Gregoriou
Download or read book Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration written by Greg N. Gregoriou and published by Springer. This book was released on 2010-12-08 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new evidence and solutions to capture the persistence observed in stock returns across developed and emerging markets.
Book Synopsis Essays on Bayesian Analysis of Financial Economics by : Liuling Li
Download or read book Essays on Bayesian Analysis of Financial Economics written by Liuling Li and published by . This book was released on 2009 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays with each essay forming a chapter. The regression models in these three chapters are different but share the same feature: the error terms of the models all follow ARMA-GARCH error processes generated either from normal or exponential power distributions. In the first chapter I present a spot asset pricing model that is known as the CKLS model. Two CKLS models are compared. In one model the ARMA-GARCH error process is generated by the exponential power distribution while in the other model the error process is generated by the normal distribution. Using monthly U.S. federal funds rate I estimate the parameters of the CKLS models. From the predictive densities I obtain the distributions of the mean squared errors of forecast (MSEF) and the predictive deviance information criterion (PDIC). In addition I use the Bayes factor and the deviance information criterion (DIC). Markov Chain Monte Carlo (MCMC) algorithms, which are stochastic numerical integration methods, are used. I find that in general the CKLS model with the error term generated by the exponential power distribution is chosen over the model with the normal error term. In the second chapter I first compare two MCMC algorithms: random walk draw and non-random walk draw for a Markov switching regression model. Two Markov switching models are compared: one with the variance of the normal distribution generated by the state space variable and the other with the constant variance. The realized volatilities of MMM Company are used to estimate and compare the models. The mean squared errors (MSE) and mean squared errors of forecast (MSEF) are used as the model selection criteria. I find that the model with the constant variance is chosen over the model with the state space variance by the MSE but the latter is chosen over the former by the MSEF. In the third chapter I estimate a bivariate copula model. Each of the two regressions is generated by the exponential power distribution. I use monthly data on SP500 and FTSE100. Results show that the correlation parameter for SP500 and FTSE100 is .6893.
Book Synopsis Linear Models and Time-Series Analysis by : Marc S. Paolella
Download or read book Linear Models and Time-Series Analysis written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-10-10 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
