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Indirect Inference Estimation Of Mixed Frequency Stochastic Volatility State Space Models Using Midas Regressions And Arch Models
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Book Synopsis Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models Using MIDAS Regressions and ARCH Models by : Patrick Gagliardini
Download or read book Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models Using MIDAS Regressions and ARCH Models written by Patrick Gagliardini and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Economic Forecasting using Time Series Methods by : Eric Ghysels
Download or read book Applied Economic Forecasting using Time Series Methods written by Eric Ghysels and published by Oxford University Press. This book was released on 2018-03-23 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable. Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online at authors' website.
Book Synopsis Estimating Stochastic Volatility Models Through Indirect Inference by : Chiara Monfardini
Download or read book Estimating Stochastic Volatility Models Through Indirect Inference written by Chiara Monfardini and published by . This book was released on 1996 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis State Space and Unobserved Component Models by : James Durbin
Download or read book State Space and Unobserved Component Models written by James Durbin and published by Cambridge University Press. This book was released on 2004-06-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.
Book Synopsis Estimation of State Space Models and Stochastic Volatility by : Shirley Miller Lira
Download or read book Estimation of State Space Models and Stochastic Volatility written by Shirley Miller Lira and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation in Stochastic Volatility Models by : Jaya P. N. Bishwal
Download or read book Parameter Estimation in Stochastic Volatility Models written by Jaya P. N. Bishwal and published by Springer Nature. This book was released on 2022-08-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Book Synopsis On the Use of High Frequency Measures of Volatility in MIDAS Regressions by : Elena Andreou
Download or read book On the Use of High Frequency Measures of Volatility in MIDAS Regressions written by Elena Andreou and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or financial variables with high frequency financial indicators' volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higher-order moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model.
Book Synopsis Fixed Interval Smoothing for State Space Models by : Howard L. Weinert
Download or read book Fixed Interval Smoothing for State Space Models written by Howard L. Weinert and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fixed-interval smoothing is a method of extracting useful information from inaccurate data. It has been applied to problems in engineering, the physical sciences, and the social sciences, in areas such as control, communications, signal processing, acoustics, geophysics, oceanography, statistics, econometrics, and structural analysis. This monograph addresses problems for which a linear stochastic state space model is available, in which case the objective is to compute the linear least-squares estimate of the state vector in a fixed interval, using observations previously collected in that interval. The author uses a geometric approach based on the method of complementary models. Using the simplest possible notation, he presents straightforward derivations of the four types of fixed-interval smoothing algorithms, and compares the algorithms in terms of efficiency and applicability. Results show that the best algorithm has received the least attention in the literature. Fixed Interval Smoothing for State Space Models: includes new material on interpolation, fast square root implementations, and boundary value models; is the first book devoted to smoothing; contains an annotated bibliography of smoothing literature; uses simple notation and clear derivations; compares algorithms from a computational perspective; identifies a best algorithm. Fixed Interval Smoothing for State Space Models will be the primary source for those wanting to understand and apply fixed-interval smoothing: academics, researchers, and graduate students in control, communications, signal processing, statistics and econometrics.
Book Synopsis Modeling Stochastic Volatility with Application to Stock Returns by : Noureddine Krichene
Download or read book Modeling Stochastic Volatility with Application to Stock Returns written by Noureddine Krichene and published by International Monetary Fund. This book was released on 2003-06 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was consistent with both volatility clustering and mean reversion. Filtering showed highly volatile markets, reflecting frequent pertinent news. Diagnostics showed no model failure, although specification improvements were always possible. The model corroborated stylized findings in volatility modeling and has potential value for market participants in asset pricing and risk management, as well as for policymakers in the design of macroeconomic policies conducive to less volatile financial markets.
Book Synopsis A Flexible State-Space Model with Application to Stochastic Volatility by : Christian Gourieroux
Download or read book A Flexible State-Space Model with Application to Stochastic Volatility written by Christian Gourieroux and published by . This book was released on 2016 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a general state-space (or latent factor) model for time series and panel data. The state process has a polynomial expansion based dynamics that can approximate any Markov dynamics arbitrarily well, and has a latent, endogenous switching regime interpretation. The resulting state-space model is associated with simulation-free, recursive formulas for prediction and filtering, as well as the maximum composite likelihood estimation method, with an extremely low computational cost. When applied to the stochastic volatility (SV) of asset returns, the model can capture, in a unified framework, stylized facts such as heavy tailed return, volatility feedback, as well as time irreversibility. The methodology is illustrated using Apple stock return data, which confirms the improvement of our model with respect to a benchmark SV model.
Book Synopsis GAUSS Programs for the Estimation of State-space Models with ARCH Errors by : Maral Kichian
Download or read book GAUSS Programs for the Estimation of State-space Models with ARCH Errors written by Maral Kichian and published by . This book was released on 2000 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to explain the use of the GAUSS programs developed to estimate a state-space model with autoregressive conditional heteroskedastic (ARCH) errors. The programs are based on the Harvey, Ruiz & Sentana (1992) paper, are flexible, and allow the user to estimate a wide variety of economic models with or without ARCH errors. The impetus for writing these programs came from the need to estimate an unobserved components model with ARCH expectations for the explicit purposes of estimating Canadian potential output and forecasting inflation. Section 2 of the paper presents the model and explains notations. Section 3 explains the GAUSS code, indicating which parts to modify in order to set up a particular model. Section 4 contains two examples that demonstrate the flexibility and limitations of the code.
Book Synopsis Estimation of Stochastic Volatility Models with Diagnostics by : A. Ronald Gallant
Download or read book Estimation of Stochastic Volatility Models with Diagnostics written by A. Ronald Gallant and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are "Semiparametric ARCH" and "Nonlinear Nonparametric". With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient.
Book Synopsis Mixed Effect Model for Absolute Log Returns of Ultra High Frequency Data by :
Download or read book Mixed Effect Model for Absolute Log Returns of Ultra High Frequency Data written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Considering absolute log returns as a proxy for stochastic volatility, the influence of explanatory variables on absolute log returns of ultra high frequency data is analysed. The irregular time structure and time dependency of the data is captured by utilizing a continuous time ARMA(p, q) process. In particular we propose a mixed effect model for the absolute log returns. Explanatory variable information is used to model the fixed effects, whereas the the error is decomposed in a non-negative Lévy driven continuous time ARMA(p, q) process and a market microstructure noise component. The parameters are estimated in a state space approach. In a small simulation study the performance of the estimators is investigated. We apply our model to IBM trade data and quantify the influence of bid-ask spread and duration on a daily basis. To verify the correlation in irregularly spaced data we use the variogram, known from spatial statistics. -- ultra high frequency ; CARMA ; mixed effect model ; state space ; Kalman filter ; variogram
Book Synopsis Modeling Volatility Using State Space Models by : Jens Timmer
Download or read book Modeling Volatility Using State Space Models written by Jens Timmer and published by . This book was released on 1997 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude due to their ignoring the distinction between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities.
Book Synopsis Frequency Domain Analysis of DSGE and Stochastic Volatility Models by : Denis Tkachenko
Download or read book Frequency Domain Analysis of DSGE and Stochastic Volatility Models written by Denis Tkachenko and published by . This book was released on 2012 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: In this dissertation, we use frequency domain methods to address issues related to identification and estimation in linearized dynamic stochastic general equilibrium (DSGE) and stochastic volatility models.The first chapter provides a necessary and sufficient condition for the local identification of the structural parameters based on the (first and) second order properties of the linearized DSGE model. The condition is flexible and simple to verify. It is extended to study identification through a subset of frequencies, partial identification, conditional identification, and constrained identification. When lack of identification is detected, the method can be used to trace out nonidentification curves. For estimation in nonsingular systems, we consider a frequency domain quasi-maximum likelihood (FDQML) estimator and present its asymptotic properties, which can be different from existing results due to the structure of the DSGE model. Finally, we discuss a quasi-Bayesian procedure for estimation and inference that can incorporate relevant prior distributions and is computationally attractive.The second chapter analyzes a popular medium scale DSGE model of Smets and Wouters (2007) using the framework developed in the previous chapter. For identification, in addition to checking parameter identifiability, we derive the corresponding nonidentification curve. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably different parameter values and impulse response functions. A further comparison between the non-parametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture.The final chapter proposes an FDQML estimator of the integrated volatility of financial assets in the noisy high frequency data setting. The approach allows for the microstructure noise to be a stationary linear process, and is analytically tractable. In practice, we approximate the noise process by a finite order autoregression, where the order is chosen using the Akaike information criterion (AIC). The simulation study shows that the finite sample performance of the estimator is very similar to its time domain analogue in the case of i.i.d. noise, and is substantially better when more sophisticated noise specifications are considered.
Book Synopsis Stochastic Volatility by : Sangjoon Kim
Download or read book Stochastic Volatility written by Sangjoon Kim and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymmetric Stable Stochastic Volatility Models by : Francisco Blasques
Download or read book Asymmetric Stable Stochastic Volatility Models written by Francisco Blasques and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
