Author: David E. AllenPublisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages :
Book Description
Asymmetric Realized Volatility Risk
Author: David E. AllenPublisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages :
Book Description
Realized Volatility Risk
Author: David E. AllenPublisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages : 38
Book Description
Asymmetric Volatility Risk
Author: Jens Carsten JackwerthPublisher:
ISBN:
Category :
Languages : en
Pages : 34
Book Description
Asymmetric volatility concerns the relation of returns to future expected volatility. Much is known from option prices about the marginal risk-neutral distributions of S&P 500 returns and of relative changes in future expected volatility (VIX). While the bivariate risk-neutral distribution cannot be inferred from the marginals, we propose a novel identification based on long-dated index options. We estimate the risk-neutral asymmetric volatility implied correlation and find it to be significantly lower than its realized counterpart. We interpret the economics of the asymmetric volatility correlation risk premium and use asymmetric volatility implied correlation to predict returns, volatility, and risk-neutral quantities.
Asymmetric Volatility Risk
Author: Jens JackwerthForecasting Realized Intra-Day Volatility and Value at Risk
Author: Stavros Antonios DegiannakisPublisher:
ISBN:
Category :
Languages : en
Pages : 24
Book Description
Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an asymmetric Autoregressive Conditional Heteroscedasticity (ARCH) model. The model is extended to also capture i) the skewness and excess kurtosis that the asset returns exhibit and ii) the fractional integration of the conditional variance. The model, which takes into consideration both the fractional integration of the conditional variance as well as the skewed and leptokurtic conditional distribution of innovations, produces the most accurate one-day-ahead volatility forecasts. The study recommends to portfolio managers and traders that extended ARCH models generate more accurate volatility forecasts of stock returns.
Market Microstructure and Nonlinear Dynamics
Author: Gilles DufrénotPublisher: Springer
ISBN: 3319052128
Category : Business & Economics
Languages : en
Pages : 322
Book Description
This book discusses market microstructure environment within the context of the global financial crisis. In the first part, the market microstructure theory is recalled and the main microstructure models and hypotheses are discussed. The second part focuses on the main effects of the financial downturn through an examination of market microstructure dynamics. In particular, the effects of market imperfections and the limitations associated with microstructure models are discussed. Finally, the new regulations and recent developments for financial markets that aim to improve the market microstructure are discussed. Well-known experts on the subject contribute to the chapters in the book. A must-read for academic researchers, students and quantitative practitioners.
Jumps, Realized Volatility and Value-at-risk
Author: Shuai YangPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
This thesis consists of three research topics, which together study the related topics of volatility jumps, modeling volatility and forecasting Value-at-Risk (VaR). The first topic focuses on volatility jumps based on two recently developed jumps detection methods and empirically studied six markets and the distributional features, size and intensity of jumps and cojumps. The results indicate that foreign exchange markets have higher jump intensities, while equity markets have a larger jump size. I find that index and stock markets have more interdependent cojumps across markets. I also find two recently proposed jump detection methods deliver contradictory results of jump and cojump properties. The jump detection technique based on realized outlyingness weighted variation (ROWV) delivers higher jump intensities in foreign exchange markets, whereas the bi-power variation (BV) method produces higher jump intensities in equity markets. Moreover, jumps under the ROWV method display more serial correlations than the BV method. The ROWV method detects more cojumps and higher cojumps intensities than the BV method does, particularly in foreign exchange markets. In the second topic, the Model Confidence Set test (MCS) is used. MCS selects superior models by power in forecasting ability. The candidate models set included 9 GARCH type models and 8 realized volatility models. The dataset is based on six markets sparming more than 10 years, avoiding the so- called data snooping problem. The dataset is extended by including recent financial crisis periods. The dc.description.abstract advantage of the MCS test is that it can compare models in a group, not only in a pair. Two loss functions that are robust to noise in volatility proxy were also implemented and the empirical results indicated that the traditional GARCH models were outperformed by realized volatility models when using intraday data. The MCS test based on MSE selected asymmetric ARFlMA models and the HAR mode as the most predictive, while the asymmetric QLike loss function revealed the leveraged HAR and leveraged HAR-CJ model based on bi-power variation as the highest performers. Moreover, results from the subsamples indicate that the asymmetric ARFIMA model performs best over turbulent periods. The third topic focuses on evaluating a broad band ofVaR forecasts. Different VaR models were compared across six markets, five volatility models, four distributions and 8 quantiles, resulting in 960 specifications. The MCS test based on regulatory favored asymmetric loss function was applied and the empirical results indicate that the proposed asymmetric ARFIMA and leveraged HAR models, coupled with generalized extreme value distribution (GEV) or generalized Pareto distribution (GPD), have the superior predictive ability on both long and short positions. The filtered extreme value methods were found to handle not only extreme quantiles but also regular ones. The analysis conducted in this thesis is intended to aid risk management, and subsequently reduce the probability of financial distress in the sector.
Asymmetric Volatility and Risk in Equity Markets
Author: Geert BekaertPublisher:
ISBN:
Category : Investments
Languages : en
Pages : 76
Book Description
It appears that volatility in equity markets is asymmetric: returns and conditional volatility are negatively correlated. We provide a unified framework to simultaneously investigate asymmetric volatility at the firm and the market level and to examine two potential explanations of the asymmetry: leverage effects and time-varying risk premiums. Our empirical application uses the market portfolio and portfolios with different leverage constructed from Nikkei 225 stocks, extending the empirical evidence on asymmetry to Japanese stocks. Although volatility asymmetry is present and significant at the market and the portfolio levels, its source differs across portfolios. We find that it is important to include leverage ratios in the volatility dynamics but that their economic effects are mostly dwarfed by the volatility feedback mechanism. Volatility feedback is enhanced by a phenomenon that we term covariance asymmetry: conditional covariances with the market increase only significantly following negative market news. We do not find significant asymmetries in conditional betas.
Beyond Greed and Fear
Author: Hersh ShefrinPublisher:
ISBN: 9780195161212
Category : Business & Economics
Languages : en
Pages : 410
Book Description
Even the best Wall Street investors make mistakes. No matter how savvy or experienced, all financial practitioners eventually let bias, overconfidence, and emotion cloud their judgement and misguide their actions. Yet most financial decision-making models fail to factor in these fundamentals of human nature. In Beyond Greed and Fear, the most authoritative guide to what really influences the decision-making process, Hersh Shefrin uses the latest psychological research to help us understand the human behavior that guides stock selection, financial services, and corporate financial strategy. Shefrin argues that financial practitioners must acknowledge and understand behavioral finance--the application of psychology to financial behavior--in order to avoid many of the investment pitfalls caused by human error. Through colorful, often humorous real-world examples, Shefrin points out the common but costly mistakes that money managers, security analysts, financial planners, investment bankers, and corporate leaders make, so that readers gain valuable insights into their own financial decisions and those of their employees, asset managers, and advisors. According to Shefrin, the financial community ignores the psychology of investing at its own peril. Beyond Greed and Fear illuminates behavioral finance for today's investor. It will help practitioners to recognize--and avoid--bias and errors in their decisions, and to modify and improve their overall investment strategies.
Handbook of Volatility Models and Their Applications
Author: Luc BauwensPublisher: John Wiley & Sons
ISBN: 0470872519
Category : Business & Economics
Languages : en
Pages : 566
Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.