Estimating Stochastic Volatility And Jumps Using High Frequency Data And Bayesian Methods
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Estimating Stochastic Volatility And Jumps Using High Frequency Data And Bayesian Methods
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2015
Estimating Stochastic Volatility And Jumps Using High Frequency Data And Bayesian Methods written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models) using daily returns. We find that both of the methods do identify continuous stochastic volatility similarly, but they do not identify similarly the jump component. Firstly - the jumps estimated using the non-parametric high-frequency estimators are much more numerous than in the case of the Bayesian method using daily data. More importantly - we find that the probabilities of jump occurrences assigned to every day by both of the methods are virtually no rank-correlated (Spearman rank correlation is 0.0148) meaning that the two methods do not identify jumps at the same days. Actually the jump probabilities inferred using the non-parametric approach are not much correlated even with the daily realized variance and the daily squared returns, indicating that the discontinuous price changes (jumps) observed on high-frequencies may not be distinguishable (from the continuous volatility) on the daily frequency. As an additional result we find strong evidence for jump size dependence and jump clustering (based on the self-exciting Hawkes process) of the jumps identified using the non-parametric method (the shrinkage estimator).
Stochastic Volatility Models With Jumps And High Frequency Data
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Author : Jonas Kau
language : en
Publisher:
Release Date : 2009
Stochastic Volatility Models With Jumps And High Frequency Data written by Jonas Kau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Bayesian Modeling And Forecasting Of 24 Hour High Frequency Volatility
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Author : Jonathan R. Stroud
language : en
Publisher:
Release Date : 2014
Bayesian Modeling And Forecasting Of 24 Hour High Frequency Volatility written by Jonathan R. Stroud and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
This paper estimates models of high frequency index futures returns using 'around the clock' 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks.
Complex Systems In Finance And Econometrics
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Author : Robert A. Meyers
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-03
Complex Systems In Finance And Econometrics written by Robert A. Meyers and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-03 with Business & Economics categories.
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
Estimating Stochastic Volatility Within A Trading Day
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Author : Sibo YAN
language : en
Publisher:
Release Date : 2017
Estimating Stochastic Volatility Within A Trading Day written by Sibo YAN and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
This thesis uses high-frequency data to characterize the volatility of asset prices within a trading day. The estimation procedure applies the generalized method of moments (GMM) to the Heston (1993) model of stochastic volatility. I apply the estimation to SPY in chapter 1 and to other 8 assets in chapter 2. I compare estimation results and discuss the implications and applicability of the model. In Chapter 3 I examine the path behavior of realized volatility and provide evidence that it is important to allow jumps in the Heston model.
Bayesian Dynamic Modeling Of High Frequency Integer Price Changes
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Author : Istvan Barra
language : en
Publisher:
Release Date : 2018
Bayesian Dynamic Modeling Of High Frequency Integer Price Changes written by Istvan Barra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the data via two different approaches: ordered probit models and discrete distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic patterns. We consider distributions with heavy tails to address occurrences of jumps in tick by tick discrete prices changes. In particular, we introduce a dynamic version of the negative binomial difference model with stochastic volatility. For each model we develop a Markov chain Monte Carlo estimation method that takes advantage of auxiliary mixture representations to facilitate the numerical implementation. This new modeling framework is illustrated by means of tick by tick data for several stocks from the NYSE and for different periods. Different models are compared with each other based on predictive likelihoods.We find evidence in favor of our preferred dynamic negative binomial difference model.
Identifying Price Jumps From Daily Data With Bayesian Vs Non Parametric Methods
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Author : Milan Fičura
language : en
Publisher:
Release Date : 2017
Identifying Price Jumps From Daily Data With Bayesian Vs Non Parametric Methods written by Milan Fičura and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Non-parametric approach to financial time series jump estimation, using the L-Estimator, is compared with the parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle Filters to estimate the out-sample evolution of its latent state variables, such as the jump occurrences. The comparison is performed on simulated time series with different kinds of dynamics, including Poisson jumps, self-exciting Hawkes jumps with long-term clustering, as well as co-jumps. In addition to that, a comparison is performed on the real world daily time series of 4 major currency exchange rates. The results from the simulation study show that for the purposes of in-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at the times at which the jumps occur, does the parametric approach achieve a similar accuracy as the non-parametric one in the case of the simulations with Poisson jumps that are relatively large, and it outperforms the non-parametric approach in the case of Hawkes jumps when the jumps are large. On the other hand, the L-Estimator provides better results than the parametric approach in all of the cases when the simulated jumps are small (1% or less), regardless of the jump process dynamics. The application of the methods to foreign exchange rate time series further shows that the estimates of the parametric method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility grows too high (as happened during the crisis). In both of these cases, the non-parametric L-Estimator based approach seems to provide more robust jump estimates, less influenced by the mentioned issues.
Stochastic Volatility And Realized Stochastic Volatility Models
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Author : Makoto Takahashi
language : en
Publisher: Springer Nature
Release Date : 2023-04-18
Stochastic Volatility And Realized Stochastic Volatility Models written by Makoto Takahashi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-18 with Business & Economics categories.
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.
Parameter Estimation In Stochastic Volatility Models
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Author : Jaya P. N. Bishwal
language : en
Publisher: Springer Nature
Release Date : 2022-08-06
Parameter Estimation In Stochastic Volatility Models written by Jaya P. N. Bishwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-06 with Mathematics categories.
This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
Estimating Stochastic Volatility Diffusion Using Conditional Moments Of Integrated Volatility
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Author : Tim Bollerslev
language : en
Publisher:
Release Date : 2001
Estimating Stochastic Volatility Diffusion Using Conditional Moments Of Integrated Volatility written by Tim Bollerslev and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Foreign exchange rates categories.