A Stochastic Volatility Model with GH Skew Student's T-distribution PDF Download
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Languages : en
Pages :
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Author:
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Author: Makoto Takahashi
Publisher: Springer Nature
ISBN: 981990935X
Category : Business & Economics
Languages : en
Pages : 120
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Book Description
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.
Author: Sebastian Trojan
Publisher:
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Category :
Languages : en
Pages : 199
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The first essay describes a very general stochastic volatility (SV) model specification with leverage, heavy tails, skew and switching regimes, using realized volatility (RV) as an auxiliary time series to improve inference on latent volatility. The information content of the range and of implied volatility using the VIX index is also analyzed. Database is the S&P 500 index. Asymmetry in the observation error is modeled by the generalized hyperbolic skew Student-t distribution, whose heavy and light tail enable substantial skewness. Resulting number of regimes and dynamics differ dependent on the auxiliary volatility proxy and are investigated in-sample for the financial crash period 2008/09 in more detail. An out-of-sample study comparing predictive ability of various model variants for a calm and a volatile period yields insights about the gains on forecasting performance from different volatility proxies. Results indicate that including RV or the VIX pays off mostly in more volatile market conditions, whereas in calmer environments SV specifications using no auxiliary series outperform. The range as volatility proxy provides a superior in-sample fit, but its predictive performance is found to be weak. The second essay presents a high frequency stochastic volatility model. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump.
Author: Toshiaki Watanabe
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 64
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Book Description
Author: Lorenzo Bergomi
Publisher: CRC Press
ISBN: 1482244071
Category : Business & Economics
Languages : en
Pages : 520
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Book Description
Packed with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does c
Author: Satyanshu K. Upadhyay
Publisher: CRC Press
ISBN: 1482235129
Category : Mathematics
Languages : en
Pages : 674
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Book Description
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples. This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.
Author: Laura García Jorcano
Publisher: Ed. Universidad de Cantabria
ISBN: 8481029122
Category : Business & Economics
Languages : en
Pages : 162
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Book Description
The thesis analyzes the effect that the sample size, the asymmetry in the distribution of returns and the leverage in their volatility have on the estimation and forecasting of market risk in financial assets. The goal is to compare the performance of a variety of models for the estimation and forecasting of Value at Risk (VaR) and Expected Shortfall (ES) for a set of assets of different nature: market indexes, individual stocks, bonds, exchange rates, and commodities. The three chapters of the thesis address issues of greatest interest for the measurement of risk in financial institutions and, therefore, for the supervision of risks in the financial system. They deal with technical issues related to the implementation of the Basel Committee's guidelines on some aspects of which very little is known in the academic world and in the specialized financial sector. In the first chapter, a numerical correction is proposed on the values usually estimatedwhen there is little statistical information, either because it is a financial asset (bond, investment fund...) recently created or issued, or because the nature or the structure of the asset or portfolio have recently changed. The second chapter analyzes the relevance of different aspects of risk modeling. The third and last chapter provides a characterization of the preferable methodology to comply with Basel requirements related to the backtesting of the Expected Shortfall.
Author: Batsirai Winmore Mazviona
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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Book Description
Author: Daniel R. Smith
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
We develop a new stochastic volatility model that captures the three most important features of stock index returns: negative correlation between returns and future volatility, excess kurtosis and negative skewness. We estimate the model parameters by maximum likelihood using a numerical integration-based filter to deal with the latent nature of volatility. In this approach different models are defined by varying the joint density of returns and future volatility conditional on current volatility. Our innovation is to construct the joint conditional density using a copula. This approach is tremendously flexible and allows the econometrician to choose the marginal distribution of both returns and volatility independently and then stitch them together using a copula, which is also chosen independently, to form the joint density. We also develop conditional moment-based model specification tests for the extent to which the various stochastic volatility models are able to capture the skewness and excess kurtosis we observe in practice. The parameter estimates and conditional moment tests indicate that leverage effects, excess kurtosis and skewness are all crucial for modeling stock returns.
