Download or read book Three Essays on Estimating Filtering and Predicting Financial Volatility written by Christian Mücher and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Improving Financial Risk Estimation Forecasting and Backtesting 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 Inside Volatility Filtering written by Alireza Javaheri and published by John Wiley & Sons. This book was released on 2015-08-24 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new, more accurate take on the classical approach to volatility evaluation Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. Base volatility estimations on more accurate data Integrate past observation with Bayesian probability Exploit posterior distribution of the hidden state for optimal estimation Boost trade profitability by utilizing "skewness" opportunities Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.

Download or read book Three Essays on Using High Frequency Data in Estimating Financial Risks written by Lidan Grossmass and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility and Correlation written by Riccardo Rebonato and published by John Wiley & Sons. This book was released on 2005-07-08 with total page 864 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Volatility and Correlation 2nd edition: The Perfect Hedger and the Fox, Rebonato looks at derivatives pricing from the angle of volatility and correlation. With both practical and theoretical applications, this is a thorough update of the highly successful Volatility & Correlation – with over 80% new or fully reworked material and is a must have both for practitioners and for students. The new and updated material includes a critical examination of the ‘perfect-replication’ approach to derivatives pricing, with special attention given to exotic options; a thorough analysis of the role of quadratic variation in derivatives pricing and hedging; a discussion of the informational efficiency of markets in commonly-used calibration and hedging practices. Treatment of new models including Variance Gamma, displaced diffusion, stochastic volatility for interest-rate smiles and equity/FX options. The book is split into four parts. Part I deals with a Black world without smiles, sets out the author’s ‘philosophical’ approach and covers deterministic volatility. Part II looks at smiles in equity and FX worlds. It begins with a review of relevant empirical information about smiles, and provides coverage of local-stochastic-volatility, general-stochastic-volatility, jump-diffusion and Variance-Gamma processes. Part II concludes with an important chapter that discusses if and to what extent one can dispense with an explicit specification of a model, and can directly prescribe the dynamics of the smile surface. Part III focusses on interest rates when the volatility is deterministic. Part IV extends this setting in order to account for smiles in a financially motivated and computationally tractable manner. In this final part the author deals with CEV processes, with diffusive stochastic volatility and with Markov-chain processes. Praise for the First Edition: “In this book, Dr Rebonato brings his penetrating eye to bear on option pricing and hedging.... The book is a must-read for those who already know the basics of options and are looking for an edge in applying the more sophisticated approaches that have recently been developed.” —Professor Ian Cooper, London Business School “Volatility and correlation are at the very core of all option pricing and hedging. In this book, Riccardo Rebonato presents the subject in his characteristically elegant and simple fashion...A rare combination of intellectual insight and practical common sense.” —Anthony Neuberger, London Business School

Download or read book Essays in Applied Econometrics of High Frequency Financial Data written by Ilya Archakov and published by . This book was released on 2016 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, co-authored with Peter Hansen and Asger Lunde, we suggest a novel approach to modeling and measuring systematic risk in equity markets. We develop a new modeling framework that treats an asset return as a dependent variable in a multiple regression model. The GARCH-type dynamics of conditional variances and correlations between the regression variables naturally imply a temporal variation of regression coefficients (betas). The model incorporates extra information from the realized (co-)variance measures extracted from high frequency data, which helps to better identify the latent covariance process and capture its changes more promptly. The suggested structure is consistent with the broad class of linear factor models in the asset pricing literature. We apply our framework to the famous three-factor Fama-French model at the daily frequency. Throughout the empirical analysis, we consider more than 800 individual stocks as well as style and sectoral exchange traded funds from the U.S. equity market. We document an appreciable cross-sectional and temporal variation of the model-implied risk loadings with the especially strong (though short-lived) distortion around the Financial Crisis episode. In addition, we find a significant heterogeneity in a relative explanatory power of the Fama-French factors across the different sectors of economy and detect a fluctuation of the risk premia estimates over time. The empirical evidence emphasizes the importance of taking into account dynamic aspects of the underlying covariance structure in asset pricing models. In the second chapter, written with Bo Laursen, we extend the popular dynamic Nelson-Siegel framework by introducing time-varying volatilities in the factor dynamics and incorporating the realized measures to improve the identification of the latent volatility state. The new model is able to effectively describe the conditional distribution dynamics of a term structure variable and can still be readily estimated with the Kalman filter. We apply our framework to model the crude oil futures prices. Using more than 150,000,000 transactions for the large panel of contracts we carefully construct the realized volatility measures corresponding to the latent Nelson-Siegel factors, estimate the model at daily frequency and evaluate it by forecasting the conditional density of futures prices. We document that the time-varying volatility specification suggested in our model strongly outperforms the constant volatility benchmark. In addition, the use of realized measures provides moderate, but systematic gains in density forecasting. In the third chapter, I investigate the rate at which information about the daily asset volatility level arrives with the transaction data in the course of the trading day. The contribution of this analysis is three-fold. First, I gauge how fast (after the market opening) the reasonable projection of the new daily volatility level can be constructed. Second, the framework provides a natural experimental field for the comparison of the small sample properties of different types of estimators as well as their (very) short-run forecasting capability. Finally, I outline an adaptive modeling framework for volatility dynamics that attaches time-varying weights to the different predictive signals in response to the changing stochastic environment. In the empirical analysis, I consider a sample of assets from the Dow Jones index. I find that the average precision of the ex-post daily volatility projections made after only 15 minutes of trading (at 9:45a.m. EST) amounts to 65% (in terms of predictive R2) and reaches up to 90% before noon. Moreover, in conjunction with the prior forecast, the first 15 minutes of trading are able to predict about 80% of the ex-post daily volatility. I document that the predictive content of the realized measures that use data at the transaction frequency is strongly superior as compared to the estimators that use sparsely sampled data, but the difference is getting negligible closer to the end of the trading day, as more observations are used to construct a projection. In the final chapter, joint with Peter Hansen, Guillaume Horel and Asger Lunde, we introduce a multivariate estimator of financial volatility that is based on the theory of Markov chains. The Markov chain framework takes advantage of the discreteness of high-frequency returns and suggests a natural decomposition of the observed price process into a martingale and a stationary components. The new estimator is robust to microstructural noise effects and is positive semidefinite by construction. We outline an approach to the estimation of high dimensional covariance matrices. This approach overcomes the curse of dimensionality caused by the tremendous number of observed price transitions (normally, exceeding 10,000 per trading day) that complicates a reliable estimation of the transition probability matrix for the multivariate Markov chain process. We study the finite sample properties of the estimator in a simulation study and apply it to high-frequency commodity prices. We find that the new estimator demonstrates a decent finite sample precision. The empirical estimates are largely in agreement with the benchmarks, but the Markov chain estimator is found to be particularly well with regards to estimating correlations.

Download or read book Forecasting Volatility in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2002-08-22 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Forecasting Volatility in the Financial Markets' assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modelling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.The editors have brought together a set of contributors that give the reader a firm grounding in relevant theory and research and an insight into the cutting edge techniques applied in this field of the financial markets.This book is of particular relevance to anyone who wants to understand dynamic areas of the financial markets.* Traders will profit by learning to arbitrage opportunities and modify their strategies to account for volatility.* Investment managers will be able to enhance their asset allocation strategies with an improved understanding of likely risks and returns.* Risk managers will understand how to improve their measurement systems and forecasts, enhancing their risk management models and controls.* Derivative specialists will gain an in-depth understanding of volatility that they can use to improve their pricing models.* Students and academics will find the collection of papers an invaluable overview of this field. This book is of particular relevance to those wanting to understand the dynamic areas of volatility modeling and forecasting of the financial marketsProvides the latest research and techniques for Traders, Investment Managers, Risk Managers and Derivative Specialists wishing to manage their downside risk exposure Current research on the key forecasting methods to use in risk management, including two new chapters

Download or read book Trends and Cycles in Financial Markets written by Jacob B.L. Smith and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is a collection of three essays applying modern time series techniques in the context of financial markets. There is a particular focus on disentangling persistent trend components from transitory cyclical dynamics. The information contained in these cyclical components is leveraged to garner insight into the broader macroeconomy. The first essay, Trend and Cycle in the Yield Curve: A Procedure for Forecasting Recessions, utilizes short-term (slope) dynamics present in the yield curve to predict impending economic downturns. Building on a large body of literature chronicling the relationship between the shape of the yield curve and the business cycle I employ Dynamic Nelson-Siegel modeling to define the level, slope, and curvature characteristics of the term structure through time. Given these dynamics, the trend and cycle are extracted using various decomposition techniques. I show that cycles present within the slope factor are extremely robust predictors of recessions, correctly identifying recessions as much as eighteen months in advance. Moreover, I develop a ``Predictive Power Score'' as a way to quantify my procedure's performance. This score demonstrates the superiority of my procedure over other common leading indicators including the yield spread. This first essay illustrates a common obstacle faced by researchers when attempting to measure cycles in real-time. Symmetric band-pass filters are estimated at the expense of data trimming, i. e. current estimates of the cycle must be sacrificed in order to construct the filtered series. Building on the work of Baxter and King (1999), Christiano and Fitzgerald (2003) construct a ``one-sided" filter which allows the practitioner to obtain estimates of the cycle in real-time. The second essay of this dissertation, Spurious Periodicity in Christiano-Fitzgerald Filtered Time Series, studies the cyclical properties of time series filtered by the Christiano and Fitzgerald (2003) filter. I show that in the presence of a stochastic trend the CF filter imposes spurious periodicity onto the filtered series, i. e. the filter imparts cyclicality where there is none. This is due to a common defect among band-pass filters which allows cyclical components of the error term to pass through the filter to the estimated cycle. In practice, this leads to cycle estimates of higher amplitude and longer duration. The third essay of this dissertation focuses on an emerging financial market which until recently has received little attention in the academic literature. An Analysis of Bitcoin Exchange Rates studies the relationship between bitcoin prices and the foreign exchange market in a way that has not been done before. I contend that the best way to think of bitcoins is as digital gold. Bitcoins are a purely electronic commodity traded for speculative purposes as well as in exchange for goods and services. Just like physical gold the relative price of bitcoins denominated in different currencies implies a nominal exchange rate. This is a departure from previous literature which treats bitcoin prices themselves as exchange rates. I argue that treating prices as exchange rates is inappropriate as one would not consider the price of physical gold to be an exchange rate. Therefore, I characterize the behavior of nominal exchange rates implied by relative bitcoin prices. I show that the implied nominal exchange rate is highly cointegrated with the nominal exchange rate determined in conventional foreign currency exchange markets. I also show that the direction of causality flows from the conventional markets to the bitcoin market and not vice-versa which can explain much of the volatility in bitcoin prices.

Download or read book Three Essays on Empirical Asset Pricing written by Wenqing Wang and published by . This book was released on 2004 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Estimation and Dynamic Modelling of Multivariate Market Risks Using High Frequency Financial Data written by Valeri Voev and published by . This book was released on 2008 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Volatility Forecasting and Forecast Evaluation written by Onno Kleen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Volatility Measurement and Modeling with Price Limits written by Rui Gao and published by . This book was released on 2014 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies volatility measurement and modeling issues when asset prices are subject to price limits based on Bayesian approaches. Two types of estimators are developed to consistently estimate integrated volatility in the presence of price limits. One is a realized volatility type estimator, but using both realized asset prices and simulated asset prices. The other is a discrete sample analogue of integrated volatility using posterior samples of the latent volatility states. These two types of estimators are first constructed based on the simple log-stochastic volatility model in Chapter 2. The simple log-stochastic volatility framework is extended in Chapter 3 to incorporate correlated innovations and further extended in Chapter 4 to accommodate jumps and fat-tailed innovations. For each framework, a MCMC algorithm is designed to simulate the unobserved asset prices, model parameters and latent states. Performances of both type estimators are also examined using simulations under each framework. Applications to Chinese stock markets are also provided.

Download or read book Essays on Volatility Forecasting and Density Estimation written by Shan Lu and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 4 compares six estimation methods for extracting risk-neutral densities (RND) from option prices. By using a pseudo-price based simulation, we find that the positive convolution approximation method provides the best performance, while mixture of two lognormals is the worst; In addition, we show that both price and volatility jumps are important components for option pricing. Our results have practical applications for policymakers as RNDs are important indicators to gauge market sentiment and expectations.

Download or read book Essays on Financial Volatility and Correlation written by George Christodoulakis and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays in International Finance written by Rita Madarassy and published by . This book was released on 2002 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Practical Guide to Forecasting Financial Market Volatility written by Ser-Huang Poon and published by John Wiley & Sons. This book was released on 2005-08-19 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.

Download or read book Three Essays in Financial Market Prediction written by Yan Liu (Emory University Graduate Student.) and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: