Volatility Asymmetry in High Frequency Data
Author | : Julia Litvinova |
Publisher | : |
Total Pages | : 220 |
Release | : 2004 |
Genre | : Rate of return |
ISBN | : |
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Author | : Julia Litvinova |
Publisher | : |
Total Pages | : 220 |
Release | : 2004 |
Genre | : Rate of return |
ISBN | : |
Author | : Turgut Kisinbay |
Publisher | : International Monetary Fund |
Total Pages | : 40 |
Release | : 2003-06-01 |
Genre | : Business & Economics |
ISBN | : 1451855303 |
Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.
Author | : Cathy Ning |
Publisher | : |
Total Pages | : |
Release | : 2009 |
Genre | : |
ISBN | : |
Author | : Tim Bollerslev |
Publisher | : |
Total Pages | : 34 |
Release | : 2008 |
Genre | : |
ISBN | : |
We examine the relationship between volatility and past and future returns in high-frequency equity market data. Consistent with a prolonged leverage effect, we find the correlations between absolute high-frequency returns and current and past high-frequency returns to be significantly negative for several days, while the reverse cross-correlations between absolute returns and future returns are generally negligible. Based on a simple aggregation formula, we demonstrate how the high-frequency data may similarly be used in more effectively assessing volatility asymmetries over longer daily return horizons. Motivated by the striking cross-correlation patterns uncovered in the high-frequency data, we investigate the ability of some popular continuous-time stochastic volatility models for explaining the observed asymmetries. Our results clearly highlight the importance of allowing for multiple latent volatility factors at very fine time scales in order to adequately describe and understand the patterns in the data.
Author | : Hao Sun |
Publisher | : |
Total Pages | : 0 |
Release | : 2020 |
Genre | : |
ISBN | : |
Modeling volatility is one of the prime objectives of financial time-series analysis. A significant feature encountered in the modeling of financial data is the asymmetric response to the volatility process of unanticipated shocks. With improvements in data acquisition, functional versions of the heteroskedastic models have emerged to deal with the high-frequency observations. Although previous studies have developed some functional time-series methods, it remains a necessity to analyze the variations in the asymmetry of the discrete model and the function model. In this study, we propose a functional threshold GARCH (fTGARCH) model and extend the news impact curve (NIC) and the cumulative impact response function (CIRF) within the functional heteroskedastic framework. We find that the fTGARCH model can describe the asymmetry of the observation data, which are revealed by the sample cross-correlation functions. The slope of the NIC changes with time for functional GARCH class models, and the changes are asymmetrical for the fTGARCH model. Using the generalized CIRF, we can explore the persistent effects of volatility for the functional GARCH class models. By fitting the models to the S&P 500 stock market index, we conclude that the fTGARCH model has some flexibility and superiority in regard to volatility asymmetry.
Author | : Supachok Thakolsri |
Publisher | : |
Total Pages | : |
Release | : 2016 |
Genre | : |
ISBN | : |
This study employs the daily data of the Stock Exchange of Thailand to test for the leverage and volatility feedback effects. The period of investigation is during January 4, 2005 to December 27, 2013, which includes the Subprime crisis period in the US that might affect the volatility of stock market return in emerging stock markets. The results from this study show that the US subprime crisis imposes a minimal positive impact on volatility. In addition, the estimations of the three parametric asymmetric volatility models give the results showing some evidence of the volatility feedback and leverage effects. The findings give implications for portfolio diversification and risk management.
Author | : Hersh Shefrin |
Publisher | : Elsevier |
Total Pages | : 636 |
Release | : 2008-05-19 |
Genre | : Business & Economics |
ISBN | : 0080482244 |
Behavioral finance is the study of how psychology affects financial decision making and financial markets. It is increasingly becoming the common way of understanding investor behavior and stock market activity. Incorporating the latest research and theory, Shefrin offers both a strong theory and efficient empirical tools that address derivatives, fixed income securities, mean-variance efficient portfolios, and the market portfolio. The book provides a series of examples to illustrate the theory. The second edition continues the tradition of the first edition by being the one and only book to focus completely on how behavioral finance principles affect asset pricing, now with its theory deepened and enriched by a plethora of research since the first edition
Author | : Stavros Degiannakis |
Publisher | : Springer |
Total Pages | : 411 |
Release | : 2016-04-29 |
Genre | : Business & Economics |
ISBN | : 1137396490 |
The global financial crisis has reopened discussion surrounding the use of appropriate theoretical financial frameworks to reflect the current economic climate. There is a need for more sophisticated analytical concepts which take into account current quantitative changes and unprecedented turbulence in the financial markets. This book provides a comprehensive guide to the quantitative analysis of high frequency financial data in the light of current events and contemporary issues, using the latest empirical research and theory. It highlights and explains the shortcomings of theoretical frameworks and provides an explanation of high-frequency theory, emphasising ways in which to critically apply this knowledge within a financial context. Modelling and Forecasting High Frequency Financial Data combines traditional and updated theories and applies them to real-world financial market situations. It will be a valuable and accessible resource for anyone wishing to understand quantitative analysis and modelling in current financial markets.
Author | : Ramazan Gençay |
Publisher | : Elsevier |
Total Pages | : 411 |
Release | : 2001-05-29 |
Genre | : Business & Economics |
ISBN | : 008049904X |
Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure. This book discusses the best mathematical models and tools for dealing with such vast amounts of data. This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets.
Author | : Francis X. Diebold |
Publisher | : Oxford University Press |
Total Pages | : 285 |
Release | : 2015-02-03 |
Genre | : Business & Economics |
ISBN | : 0199338329 |
Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.