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The One-Trading-Day-Ahead Forecast Errors of Intra-Day Realized Volatility

The One-Trading-Day-Ahead Forecast Errors of Intra-Day Realized Volatility
Author: Stavros Antonios Degiannakis
Publisher:
Total Pages: 32
Release: 2018
Genre:
ISBN:
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Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts. We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.

Forecasting Daily Stock Volatility

Forecasting Daily Stock Volatility
Author: Ana-Maria Fuertes
Publisher:
Total Pages: 72
Release: 2013
Genre:
ISBN:
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Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t-1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.

Modelling and Forecasting High Frequency Financial Data

Modelling and Forecasting High Frequency Financial Data
Author: Stavros Degiannakis
Publisher: Springer
Total Pages: 411
Release: 2016-04-29
Genre: Business & Economics
ISBN: 1137396490
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The global financial crisis has reopened discussion surrounding the use of appropriate theoretical financial frameworks to reflect the current economic climate. There is a need for more sophisticated analytical concepts which take into account current quantitative changes and unprecedented turbulence in the financial markets. This book provides a comprehensive guide to the quantitative analysis of high frequency financial data in the light of current events and contemporary issues, using the latest empirical research and theory. It highlights and explains the shortcomings of theoretical frameworks and provides an explanation of high-frequency theory, emphasising ways in which to critically apply this knowledge within a financial context. Modelling and Forecasting High Frequency Financial Data combines traditional and updated theories and applies them to real-world financial market situations. It will be a valuable and accessible resource for anyone wishing to understand quantitative analysis and modelling in current financial markets.

Forecasting Realized Intra-Day Volatility and Value at Risk

Forecasting Realized Intra-Day Volatility and Value at Risk
Author: Stavros Antonios Degiannakis
Publisher:
Total Pages: 24
Release: 2018
Genre:
ISBN:
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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.

Analysing Intraday Implied Volatility for Pricing Currency Options

Analysing Intraday Implied Volatility for Pricing Currency Options
Author: Thi Le
Publisher: Springer Nature
Total Pages: 350
Release: 2021-04-13
Genre: Business & Economics
ISBN: 3030712427
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This book focuses on the impact of high-frequency data in forecasting market volatility and options price. New technologies have created opportunities to obtain better, faster, and more efficient datasets to explore financial market phenomena at the most acceptable data levels. It provides reliable intraday data supporting financial investment decisions across different assets classes and instruments consisting of commodities, derivatives, equities, fixed income and foreign exchange. This book emphasises four key areas, (1) estimating intraday implied volatility using ultra-high frequency (5-minutes frequency) currency options to capture traders' trading behaviour, (2) computing realised volatility based on 5-minute frequency currency price to obtain speculators' speculation attitude, (3) examining the ability of implied volatility to subsume market information through forecasting realised volatility and (4) evaluating the predictive power of implied volatility for pricing currency options. This is a must-read for academics and professionals who want to improve their skills and outcomes in trading options.

Handbook of Quantitative Finance and Risk Management

Handbook of Quantitative Finance and Risk Management
Author: Cheng-Few Lee
Publisher: Springer Science & Business Media
Total Pages: 1700
Release: 2010-06-14
Genre: Business & Economics
ISBN: 0387771174
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Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This two-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From "arbitrage" to "yield spreads," the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.

A Practical Guide to Forecasting Financial Market Volatility

A Practical Guide to Forecasting Financial Market Volatility
Author: Ser-Huang Poon
Publisher: John Wiley & Sons
Total Pages: 236
Release: 2005-08-19
Genre: Business & Economics
ISBN: 0470856157
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Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.

Forecasting Stock Volatility

Forecasting Stock Volatility
Author: Xingyi Li
Publisher:
Total Pages: 33
Release: 2018
Genre:
ISBN:
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There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as compared with that delivered by the models that use daily data. Exactly how much better is still unknown. The present paper fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important directions. First, we employ an extensive set of intraday data on 31 individual stocks over a sample period of 19 years. Second, we use forecast horizons ranging from 1 day to 6 months. Third, we evaluate the precision of volatility forecast provided by various competing models. Fourth, we conduct several robustness checks to assess the sensitivity of our results to various alternative choices. The major finding of our empirical study is that the gains from using intraday data are rather significant and persist over longer forecast horizons. Depending on the forecast horizon, the improvement in forecast precision varies from 30 to 50 percent. We demonstrate that our main results on the forecast accuracy gains are robust to the choice of intraday data frequency and the choice of measure of realized daily volatility.

Forecasting the Realized Variance in the Presence of Intraday Periodicity

Forecasting the Realized Variance in the Presence of Intraday Periodicity
Author: Ana-Maria H. Dumitru
Publisher:
Total Pages:
Release: 2019
Genre:
ISBN:
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This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.