Download or read book Localized Quantile Regression of Realized Volatility written by Janaki Koralage and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility is a financial term that measures the dispersion of asset returns. Calculating and predicting volatility are not simple, but there are several well-known models for determining the volatility of assets. In recent years, researchers have been interested in developing statistical methods to model financial volatility, and new concepts have been applied to achieve better results. Quantile regression is another area gaining increased attention in the analysis of financial data. In this thesis, we propose a new quantile regression model for measuring the volatility of financial assets called the localized quantile regression model. As the name suggests, the proposed model is a local model rather than a global model. It takes care of possible structural changes and makes predictions of volatility more reliable. The initial step in this approach is to identify the longest interval of homogeneity. Identifying this interval of homogeneity involves a sequential testing procedure. After identifying intervals, we can apply quantile regression for each homogeneous time interval. The main advantage of this method is that it does not require any distributional assumptions. Simulation studies are carried out to investigate the performance of the proposed method. Results obtained from the simulation study show that the localized quantile regression model is appropriate for modeling the volatility of financial assets.
Download or read book The Quantile Heterogeneous Autoregressive Model of Realized Volatility written by Konstantin Kuck and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: We provide a comprehensive view on volatility dynamics in precious metals and crude oil markets. Using high-frequency futures data, we construct realized volatilities and estimate (Quantile) Heterogeneous Autoregressive models for the daily volatility of Gold, Silver and Crude Oil futures. We model realized volatility as a linear function of lagged realized volatility measured over different time resolutions to explicitly account for the potentially heterogeneous impact of market participants with different trading motives and investment horizons. Using quantile regression allows us to identify potential non-linearities and asymmetries in the short-, mid- and long-term autoregressive dynamics with respect to different levels of current volatility. We document considerable changes in the relative importance of short-, mid-, and long-term volatility components under varying market conditions. The patterns that we identify are remarkably similar across the three assets. Specifically, past daily and monthly volatility have a strong impact on today's volatility, when current volatility is low (lower quantiles of the volatility distribution). The effect of past weekly volatility, however, increases distinctly from lower to higher quantiles of the conditional volatility distribution. The results might indicate considerable investor attention shifts and changes in the proportions of traders with different time horizons.
Download or read book Local Quantile Regression written by Wolfgang Härdle and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Think Again written by Dirk G. Baur and published by . This book was released on 2017 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: We use quantile regressions to demonstrate that volatility persistence and the asymmetric "leverage" effect are high volatility phenomena. More specifically, we find that (i) low volatility is not persistent, but high volatility all the more, even featuring properties of explosive processes; (ii) both positive and negative shocks increase volatility but negative shocks display a stronger effect; and (iii) jumps do neither drive nor destroy the persistence of volatility. The analysis illustrates that quantile regression can provide information that is hidden in commonly used GARCH or realized volatility models. The quantile regression results also explain the weak empirical evidence of the leverage effect and the volatility feedback effect.
Download or read book Quantile Regression Estimation of Stock Market Volatility and Its Causes written by Alabi Oluwapelumi and published by . This book was released on 2017 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market volatility is the amount of uncertainty or risk about the size of changes in stock market security value. In this study, GARCH model was built to generate stock price volatility and quantile regression estimation was used to determine the cause of volatility in stock market at different quantile level. The study provides the graphical presentation of the coefficients estimated and the variables employed. The results of the study showed that the previous residuals (ARCH effect) are significantly contributed to stock market volatility at lower quantile level (0.1, 0.25, and 0.5) and the previous volatility significant only at higher quantile level (0.9), while only exchange rate return is significant among the external causes considered.
Download or read book Examining the Dynamic Relationship Between Volatility and Stock Returns Using Quantile Regression Approach written by 楊青融 and published by . This book was released on 2009 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Inference with Quantile Regression in Stochastic Volatility Models with Application to Value at Risk Calculation written by Paramita Saha and published by . This book was released on 2008 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: RQMM, SV, Quantile Regression, VaR, Indirect Inference.
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.
Download or read book Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging written by Zongwu Cai and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Localized Realized Volatility Modelling written by Ying Chen and published by . This book was released on 2017 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the recent availability of high-frequency financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability.
Download or read book Time Series Analysis Methods and Applications written by Tata Subba Rao and published by Elsevier. This book was released on 2012-06-26 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.
Download or read book Localized Realized Volatility Modelling written by Ying Chen and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Analysis Methods and Applications written by and published by Elsevier. This book was released on 2012-05-18 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. - Comprehensively presents the various aspects of statistical methodology - Discusses a wide variety of diverse applications and recent developments - Contributors are internationally renowened experts in their respective areas
Download or read book Stock Market Analysis Volatility Forcasting and Effect of Macroeconomic Variables written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Combined Estimation for Quantile Regression written by Kehui Wang and published by . This book was released on 2015 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantile Forecast Combinations in Realised Volatility Prediction written by Loukia Meligkotsidou and published by . This book was released on 2015 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior quantile, density and point predictive performance relative to the univariate models and the autoregressive benchmark.
Download or read book Stock Market Analysis Volatility Forecasting and Effect of Macroeconimic Variables written by Yixiu Zhao and published by . This book was released on 2018 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:
