Forecasting Volatility With Empirical Similarity And Google Trends
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Forecasting Volatility With Empirical Similarity And Google Trends
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Author : Moritz Heiden
language : en
Publisher:
Release Date : 2015
Forecasting Volatility With Empirical Similarity And Google Trends written by Moritz Heiden 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.
This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The practical implications for risk management are highlighted in a Value-at-Risk forecasting exercise, where our model produces significantly more accurate forecasts while requiring less capital due to fewer overpredictions.
Forecasting Volatility
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Author : Federico Baldi Lanfranchi
language : en
Publisher:
Release Date : 2018
Forecasting Volatility written by Federico Baldi Lanfranchi 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.
Accurately forecasting volatility is key in many financial applications. In this study, I suggest that individuals gather information online before implementing their trading decisions. In periods of higher investor concern, online information seeking intensifies. By analysing Google search data for a selected set of keywords, I find that changes in Google hits lead changes in market volatility. I show that a regressor based on search engine data can provide a meaningful complement to a two-factor EGARCH model. Results suggest that the augmented model significantly outperforms its restricted counterpart from a forecasting perspective.
In Search Of Information Use Of Google Trends Data To Narrow Information Gaps For Low Income Developing Countries
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Author : Mr.Futoshi Narita
language : en
Publisher: International Monetary Fund
Release Date : 2018-12-14
In Search Of Information Use Of Google Trends Data To Narrow Information Gaps For Low Income Developing Countries written by Mr.Futoshi Narita and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-14 with Business & Economics categories.
Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.
The Importance Of Being Informed Forecasting Market Risk Measures For The Russian Rts Index Future Using Online Data And Implied Volatility Over Two Decades
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Author : Dean Fantazzini
language : en
Publisher: Litres
Release Date : 2022-01-29
The Importance Of Being Informed Forecasting Market Risk Measures For The Russian Rts Index Future Using Online Data And Implied Volatility Over Two Decades written by Dean Fantazzini and has been published by Litres this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-29 with Computers categories.
This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of daily data from 2006 till 2019, which includes several episodes of large-scale turbulence in the Russian future market. We found that the predictive power of several models did not increase if these two variables were added, but actually decreased.The worst results were obtained when these two variables were added jointly and during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with these variables did not reach numerical convergence. Our empirical evidence shows that, in the case of Russian future markets, TGARCH models with implied volatility and Student’s t errors are better choices if robust market risk measures are of concern.
The Empirical Similarity Approach For Volatility Prediction
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Author : Vasyl Golosnoy
language : en
Publisher:
Release Date : 2014
The Empirical Similarity Approach For Volatility Prediction written by Vasyl Golosnoy 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.
In this paper we adapt the empirical similarity (ES) concept for the purpose of combining forecasts originating from different models. Our ES approach is suitable for situations where a decision maker refrains from evaluating success probabilities of forecasting models but prefers to think by analogy. It allows to determine weights of the forecasting combination by quantifying distances between model predictions and corresponding realizations of the process of interest as they are perceived by decision makers. The proposed ES approach is applied for combining models in order to forecast daily volatility of the major stock market indices.
Google Trends Predict Stock Volatility
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Author : Christopher Siergiej
language : en
Publisher:
Release Date : 2015
Google Trends Predict Stock Volatility written by Christopher Siergiej 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.
The thesis studies the effect of weekly search volume data from Google Trends on volatility measures of a portfolio of hand-picked stocks. Twelve stocks were selected from three sectors and a Granger causality analysis was performed to determine whether the search volume time series was useful in forecasting the volatility time series for a given stock. The re- sults from the Granger causality analysis showed that some, but not all, stocks could use their search volume data from Google Trends to signifi- cantly forecast their volatility. For those stocks whose search volume data proved fruitful in forecasting their volatility, a search volume model con- sisting of lags of search volume data as predictors was compared to a null model consisting of the average of the volatility as a forecast. Using the mean absolute percentage error as a metric, the results support the view that the search volume model does have some forecast ability in produc- ing volatility estimates.
Decision Economics Complexity Of Decisions And Decisions For Complexity
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Author : Edgardo Bucciarelli
language : en
Publisher: Springer Nature
Release Date : 2020-02-07
Decision Economics Complexity Of Decisions And Decisions For Complexity written by Edgardo Bucciarelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-07 with Technology & Engineering categories.
This book is based on the International Conference on Decision Economics (DECON 2019). Highlighting the fact that important decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, psychology, small and international business, management, operations, and production, the book focuses on analytics as an emerging synthesis of sophisticated methodology and large data systems used to guide economic decision-making in an increasingly complex business environment. DECON 2019 was organised by the University of Chieti-Pescara (Italy), the National Chengchi University of Taipei (Taiwan), and the University of Salamanca (Spain), and was held at the Escuela politécnica Superior de Ávila, Spain, from 26th to 28th June, 2019. Sponsored by IEEE Systems Man and Cybernetics Society, Spain Section Chapter, and IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA-and-APPIA, with the funding supporting of the Junta de Castilla y León, Spain (ID: SA267P18-Project co-financed with FEDER funds)
Ai And Financial Markets
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Author : Shigeyuki Hamori
language : en
Publisher: MDPI
Release Date : 2020-07-01
Ai And Financial Markets written by Shigeyuki Hamori and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Business & Economics categories.
Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
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Author : 王闻
language : zh-CN
Publisher: BEIJING BOOK CO. INC.
Release Date : 2023-01-01
written by 王闻 and has been published by BEIJING BOOK CO. INC. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.
所谓另类数据，就其本意，就是非传统数据。目前业内对于另类数据并没有统一的定义，大体上可以理解为有别于传统数据同时有价值的数据和信息。当下越来越多的数据渗入我们的生活中，但是数据本身具有洞见的时候才会有用。随着人工智能和机器学习的发展，我们可以从看似杂乱无章的海量数据中提取特征，云计算的发展也可以有效管理此类数据流，并且高效地训练模型。从国际范围来看，诸多不同类型的另类数据已经应用到与经济相关的很多领域，包括太空中卫星传来的图像，社交媒体上的帖文，大型商场中的客流量等，不一而足，但是中文图书市场尚未有书籍对另类数据进行系统性地讨论和分析，而本书就弥补了图书市场的这个空白。 《数字经济系列02-另类数据：理论与实践》共有8章。第1章到第6章详细介绍了另类数据的概念、分类、挑战和风险、应用流程和价值等方面的内容。第7章和第8章则分别介绍了不同类型的另类数据。
Selfies As A Mode Of Social Media And Work Space Research
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Author : Hai-Jew, Shalin
language : en
Publisher: IGI Global
Release Date : 2017-11-30
Selfies As A Mode Of Social Media And Work Space Research written by Hai-Jew, Shalin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-30 with Computers categories.
The Western cultural trend of self-representation is transcending borders as it permeates the online world. A prime example of this trend is selfies, and how they have evolved into more than just self-portraits. Selfies as a Mode of Social Media and Work Space Research is a comprehensive reference source for the latest research on explicit and implicit messaging of self-portraiture and its indications about individuals, groups, and societies. Featuring coverage on a broad range of topics including dating, job hunting, and marketing, this publication is ideally designed for academicians, researchers, and professionals interested in the current phenomenon of selfies and their impact on society.