Coupling High Frequency Data With Nonlinear Models In Multiple Step Ahead Forecasting Of Energy Markets Volatility PDF Download
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| Author | : Jozef Baruník |
| Publisher | : |
| Total Pages | : 34 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Download Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility Book in PDF, ePub and Kindle
In the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specific estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the pre-crisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with artificial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.
| Author | : Jonathan H. Wright |
| Publisher | : |
| Total Pages | : 38 |
| Release | : 1999 |
| Genre | : Rate of return |
| ISBN | : |
Download High Frequency Data, Frequency Domain Inference and Volatility Forecasting Book in PDF, ePub and Kindle
While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.
| Author | : Yoshihisa Inada |
| Publisher | : World Scientific |
| Total Pages | : 126 |
| Release | : 2018-03-08 |
| Genre | : Business & Economics |
| ISBN | : 9813232382 |
Download A Dynamic Use Of Survey Data And High Frequency Model Forecasting Book in PDF, ePub and Kindle
This volume investigates the accuracy and dynamic performance of a high-frequency forecast model for the Japanese and United States economies based on the Current Quarter Model (CQM) or High Frequency Model (HFM) developed by the late Professor Emeritus Lawrence R. Klein. It also presents a survey of recent developments in high-frequency forecasts and gives an example application of the CQM model in forecasting Gross Regional Products (GRPs).
| Author | : Ben Charoenwong |
| Publisher | : |
| Total Pages | : 19 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Download Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility? Book in PDF, ePub and Kindle
No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations of residual serial independence are compounded in temporal aggregation. In this paper, we show that the conditional autocorrelation in return residuals is a strong predictor of the relative performance of different frequency models of volatility. When the conditional autocorrelation is high, the higher frequency model performs markedly worse than its lower frequency counterpart. Empirically, we show that residual autocorrelation exists in the large cross section of stocks at any given point in time, and that this misspecification can substantially decrease the accuracy of multi-step forecasts generated by higher frequency models. Comparing the monthly volatility predictions from daily models and monthly models, we show that there is a trade-off between the gains from high-frequency data and the susceptibility of its multi-period ahead forecasts to returns model misspecification.
| Author | : Lars Dannecker |
| Publisher | : Springer |
| Total Pages | : 241 |
| Release | : 2015-08-06 |
| Genre | : Computers |
| ISBN | : 3658110392 |
Download Energy Time Series Forecasting Book in PDF, ePub and Kindle
Lars Dannecker developed a novel online forecasting process that significantly improves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Improving the forecasting efficiency is a key pre-requisite for ensuring stable electricity grids in the face of an increasing amount of renewable energy sources. It is also important to facilitate the move from static day ahead electricity trading towards more dynamic real-time marketplaces. The online forecasting process is realized by a number of approaches on the logical as well as on the physical layer that we introduce in the course of this book. Nominated for the Georg-Helm-Preis 2015 awarded by the Technische Universität Dresden.
| Author | : Holger Kömm |
| Publisher | : Springer Gabler |
| Total Pages | : 0 |
| Release | : 2016-02-16 |
| Genre | : Business & Economics |
| ISBN | : 9783658125950 |
Download Forecasting High-Frequency Volatility Shocks Book in PDF, ePub and Kindle
This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing estimators to compute the unobservable volatility; second, a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and third, a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and nonshock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.
| Author | : Marcel Prokopczuk |
| Publisher | : |
| Total Pages | : 81 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Download Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets Book in PDF, ePub and Kindle
This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons and loss functions.
| Author | : Yujia Hu |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
Download Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection Book in PDF, ePub and Kindle
An instant may matter for the course of an entire life. It is with this idea that the present research had its inception. High frequency financial data are becoming increasingly available and this has triggered research in financial econometrics where information at high frequency can be exploited for different purposes. The most prominent example of this is the estimation and forecast of financial volatility. The research, chapter by chapter is summarized below. Chapter 1 provides empirical evidence on univariate realized volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. It examines leverage and volatility feedback effects among continuous and jump components of the S & P500 price and volatility dynamics, using recently developed methodologies to detect jumps and to disentangle their size from the continuous return and the continuous volatility. The research finds that jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improves the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative than positive jump semivariance. A simple autoregressive model is proposed and this is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated. Chapter 2 investigates the out-of-sample performance and the economic value of multivariate forecasting models for volatility of exchange rate returns. It finds that, when the realized covariance matrix approximates the true latent covariance, a model that uses high frequency information for the correlation is more appropriate compared to alternative models that uses only low-frequency data. However multivariate FX returns standardized by the.
| Author | : Francesca Lilla |
| Publisher | : |
| Total Pages | : 37 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Download High Frequency Vs. Daily Resolution Book in PDF, ePub and Kindle
Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset's liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing fat-tails) and leverage effects translates in more accurate VaR measure.
| Author | : Riccardo Rebonato |
| Publisher | : John Wiley & Sons |
| Total Pages | : 864 |
| Release | : 2005-07-08 |
| Genre | : Business & Economics |
| ISBN | : 0470091401 |
Download Volatility and Correlation Book in PDF, ePub and Kindle
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
