Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Estimating Stochastic Volatility Models Through Indirect Inference
Download Estimating Stochastic Volatility Models Through Indirect Inference full books in PDF, epub, and Kindle. Read online Estimating Stochastic Volatility Models Through Indirect Inference ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
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 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose as a tool for the estimation of stochastic volatility models two indirect inference estimators based on the choice of an autoregressive auxiliary model and an ARMA auxiliary model, respectively. These choices make the auxiliary parameter easy to estimate and at the same time allow the derivation of optimal indirect inference estimators. The results of some Monte Carlo experiments provide evidence that the indirect inference estimators perform well in finite sample, although less efficiently than Bayes and Simulated EM algorithms.
Book Synopsis Indirect Inference and Its Application to Empirical Finance by : Mario Philipp Rothfelder
Download or read book Indirect Inference and Its Application to Empirical Finance written by Mario Philipp Rothfelder and published by . This book was released on 2010 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimatin Stochastic Volatility Models Through Indirect Inference by : Chiara Monfardini
Download or read book Estimatin Stochastic Volatility Models Through Indirect Inference written by Chiara Monfardini and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 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 Indirect Inference with Time Series Observed with Error by : Eduardo Rossi
Download or read book Indirect Inference with Time Series Observed with Error written by Eduardo Rossi and published by . This book was released on 2018 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. Instead, if the condition for identification is satisfied, the measurement error parameters can be estimated jointly with the structural ones leading to a consistent and asymptotically Gaussian estimator. The issues of identification and misspecification of ME are discussed in detail. Based on the encompassing principle, we show that II can still be consistent for the parameters of interest when the conditional distribution of ME is misspecified, as long as the structural model for the observed series encompasses the auxiliary. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure noise. The empirical application stresses the importance of a realistic specification of the microstructure noise distribution to match the features of the observed log-returns at high frequencies.
Book Synopsis A Simple Approach to the Estimation of Continuous Time CEV Stochastic Volatility Models of the Short-term Rate by : Fabio Fornari
Download or read book A Simple Approach to the Estimation of Continuous Time CEV Stochastic Volatility Models of the Short-term Rate written by Fabio Fornari and published by . This book was released on 2001 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Inferences in Volatility Models by : Vickneswary Tagore
Download or read book Inferences in Volatility Models written by Vickneswary Tagore and published by . This book was released on 2010 with total page 220 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.
Book Synopsis Stochastic Volatility and Realized Stochastic Volatility Models by : Makoto Takahashi
Download or read book Stochastic Volatility and Realized Stochastic Volatility Models written by Makoto Takahashi and published by Springer Nature. This book was released on 2023-04-18 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.
Download or read book U-MIDAS written by Claudia Foroni and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inference for Stochastic Volatility Models by : Md. Nazmul Ahsan
Download or read book Statistical Inference for Stochastic Volatility Models written by Md. Nazmul Ahsan and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Although stochastic volatility (SV) models have many appealing features, estimation and inference on SV models are challenging problems due to the inherent difficulty of evaluating the likelihood function. The existing methods are either computationally costly and/or inefficient. This thesis studies and contributes to the SV literature from the estimation, inference, and volatility forecasting viewpoints. It consists of three essays, which include both theoretical and empirical contributions. On the whole, the thesis develops easily applicable statistical methods for stochastic volatility models.The first essay proposes computationally simple moment-based estimators for the first-order SV model. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that the proposed estimators match (or exceed) alternative estimators in terms of precision – including Bayesian estimators proposed in this context, which have the best performance among alternative estimators. Using this simple estimator, we study three crucial test problems (no persistence, no latent specification of volatility, and no stochastic volatility hypothesis), and evaluate these null hypotheses in three ways: asymptotic critical values, a parametric bootstrap procedure, and a maximized Monte Carlo procedure. The proposed methods are applied to daily observations on the returns for three major stock prices [Coca-Cola, Walmart, Ford], and the Standard and Poor’s Composite Price Index. The results show the presence of stochastic volatility with strong persistence.The second essay studies the problem of estimating higher-order stochastic volatility [SV(p)] models. The estimation of SV(p) models is even more challenging and rarely considered in the literature. We propose several estimators for higher-order stochastic volatility models. Among these, the simple winsorized ARMA-based estimator is uniformly superior in terms of bias and RMSE to other estimators, including the Bayesian MCMC estimator. The proposed estimators are applied to stock return data, and the usefulness of the proposed estimators is assessed in two ways. First, using daily returns on the S&P 500 index from 1928 to 2016, we find that higher-order SV models – in particular an SV(3) model – are preferable to an SV(1), from the viewpoints of model fit and both asymptotic and finite-sample tests. Second, using different volatility proxies (squared return and realized volatility), we find that higher-order SV models are preferable for out-of-sample volatility forecasting, whether a high volatility period (such as financial crisis) is included in the estimation sample or the forecasted sample. Our results highlight the usefulness of higher-order SV models for volatility forecasting.In the final essay, we introduce a novel class of generalized stochastic volatility (GSV) models which utilize high-frequency (HF) information (realized volatility (RV) measures). GSV models can accommodate nonstationary volatility process, various distributional assumptions, and exogenous regressors in the latent volatility equation. Instrumental variable methods are employed to provide a unified framework for the analysis (estimation and inference) of GSV models. We consider the parameter inference problem in GSV models with nonstationary volatility and develop identification-robust methods for joint hypotheses involving the volatility persistence parameter and the autocorrelation parameter of the composite error (or the noise ratio). For distributional theory, three different sets of assumptions are considered. In simulations, the proposed tests outperform the usual asymptotic test regarding size and exhibit excellent power. We apply our inference methods to IBM price and option data andidentify several empirical relationships"--
Book Synopsis Robust Inference with Quantile Regression in Stochastic Volatility Models with Application to Value at Risk Calculation by :
Download or read book Robust Inference with Quantile Regression in Stochastic Volatility Models with Application to Value at Risk Calculation written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Volatility (SV) models play an integral role in modeling time varying volatility, with widespread application in finance. Due to the absence of a closed form likelihood function, estimation is a challenging problem. In the presence of outliers, and the high kurtosis prevalent in financial data, robust estimation techniques are desirable. Also, in the context of risk assessment when the underlying model is SV, computing the one step ahead predictive return densities for Value at Risk (VaR) calculation entails a numerically indirect procedure. The Quantile Regression (QR) estimation is an increasingly important tool for analysis, which helps in fitting parsimonious models in lieu of full conditional distributions. We propose two methods (i) Regression Quantile Method of Moments (RQMM) and (ii) Regression Quantile - Kalman Filtering method (RQ-KF) based on the QR approach that can be used to obtain robust SV model parameter estimates as well as VaR estimates. The RQMM is a simulation based indirect inference procedure where auxiliary recursive quantile models are used, with gradients of the RQ objective function providing the moment conditions. This was motivated by the Efficient Method of Moments (EMM) approach used in SV model estimation and the Conditional Autoregressive Value at Risk (CAViaR) method. An optimal linear quantile model based on the underlying SV assumption is derived. This is used along with other CAViaR specifications for the auxiliary models. The RQ-KF is a computationally simplified procedure combining the QML and QR methodologies. Based on a recursive model under the SV framework, quantile estimates are produced by the Kalman filtering scheme and are further refined using the RQ objective function, yielding robust estimates. For illustration purposes, comparison of the RQMM method with EMM under different data scenarios show that RQMM is stable under model misspecification, presence of outliers and heavy-tailedness. Comparison of the RQ.
Book Synopsis Stochastic Volatility in Financial Markets by : Antonio Mele
Download or read book Stochastic Volatility in Financial Markets written by Antonio Mele and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Volatility in Financial Markets presents advanced topics in financial econometrics and theoretical finance, and is divided into three main parts. The first part aims at documenting an empirical regularity of financial price changes: the occurrence of sudden and persistent changes of financial markets volatility. This phenomenon, technically termed `stochastic volatility', or `conditional heteroskedasticity', has been well known for at least 20 years; in this part, further, useful theoretical properties of conditionally heteroskedastic models are uncovered. The second part goes beyond the statistical aspects of stochastic volatility models: it constructs and uses new fully articulated, theoretically-sounded financial asset pricing models that allow for the presence of conditional heteroskedasticity. The third part shows how the inclusion of the statistical aspects of stochastic volatility in a rigorous economic scheme can be faced from an empirical standpoint.
Book Synopsis Estimation and Inference with the Efficient Method of Moment by : Pieter Jelle van der Sluis
Download or read book Estimation and Inference with the Efficient Method of Moment written by Pieter Jelle van der Sluis and published by . This book was released on 1999 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimating Stochastic Volatility Models Using Predictionbased Estimating Functions by : Asger Lunde
Download or read book Estimating Stochastic Volatility Models Using Predictionbased Estimating Functions written by Asger Lunde and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
