Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets


Book Description

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.




Do Jumps Matter in Realized Volatility Modeling and Forecasting? Empirical Evidence and a New Model


Book Description

Building on an extensive empirical analysis I investigate the relevance of jumps and signed variations in predicting Realized Volatility. I show that properly accounting for intra-day volatility patterns and staleness sensibly reduces the identified jumps. Realized Variance decompositions based on intra-day return size and sign improve the in-sample fit of the models commonly adopted in empirical studies. I also introduce a novel specification based on a more informative decomposition of Realized Volatility, which offer improvements over standard models. From a forecasting perspective, the empirical evidence I report shows that most models, irrespective of their flexibility, are statistically equivalent in many cases. This result is confirmed with different samples, liquidity levels, forecast horizons and possible transformations of the dependent and explanatory variables.




Volatility Forecasting


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Forecasting the Volatility of Stock Market and Oil Futures Market


Book Description

The volatility has been one of the cores of the financial theory research, in addition to the stock markets and the futures market are an important part of modern financial markets. Forecast volatility of the stock market and oil futures market is an important part of the theory of financial markets research.




Research on the Volatility of Oil Futures and European Stock Markets


Book Description

The volatility has been one of the cores of the financial theory research, in addition to the futures market is an important part of modern financial markets, the futures market volatility is an important part of the theory of financial markets research.




Stochastic Processes with Applications


Book Description

Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines. This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included.




Forecasting Volatility in the Financial Markets


Book Description

Forecasting Volatility in the Financial Markets, Third Edition 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 provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling




Volatility Forecasting


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Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting


Book Description

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not only consistent, but also scarcely plagued by small-sample bias. To this purpose, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S & P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump. -- Volatility estimation ; jump detection ; volatility forecasting ; threshold estimation ; financial markets




The Sensitivity of Implied Volatility to Expectations of Jumps in Volatility


Book Description

The apparent bias in implied volatility as a forecast of the subsequently realized volatility is a well-documented empirical puzzle. As suggested by e.g. Feinstein (1989), Jackwerth and Rubinstein (1996), and Bates (1997), we test whether unrealized expectations of jumps in volatility could explain this phenomenon. Our findings show that expectations of infrequently occurring jumps in volatility are priced in implied volatility, which has two important consequences. First, implied volatility will slightly exceed realized volatility most of the time only to be considerably lower than realized volatility during infrequently occurring periods of very high volatility. Second, the slope coefficient in the classic forecasting regression of realized volatility on implied volatility is very sensitive to the discrepancy between the ex ante expected and ex post realized jump frequencies. If the in-sample frequency of positive volatility jumps is lower than ex ante assessed by the market, the slope coefficient will be biased downward and the classic regression test will erroneously reject the hypothesis of no bias even if the market is informationally efficient. Since the inferences of almost all previous studies on the forecasting power of implied volatility have been based on data from a period of historically low volatility, our results provide a rational explanation for the illusory bias in implied volatility.