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Author: Moritz Heiden
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Moritz Heiden
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Federico Baldi Lanfranchi
Publisher:
ISBN:
Category :
Languages : en
Pages : 10
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Book Description
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.
Author: Mr.Futoshi Narita
Publisher: International Monetary Fund
ISBN: 1484390172
Category : Business & Economics
Languages : en
Pages : 51
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Book Description
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.
Author: Christopher Siergiej
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author: Dean Fantazzini
Publisher: Litres
ISBN: 5042017135
Category : Computers
Languages : en
Pages : 27
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Book Description
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.
Author: Vasyl Golosnoy
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
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Book Description
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.
Author: Edgardo Bucciarelli
Publisher: Springer Nature
ISBN: 3030382273
Category : Technology & Engineering
Languages : en
Pages : 334
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Book Description
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)
Author: Shigeyuki Hamori
Publisher: MDPI
ISBN: 3039362240
Category : Business & Economics
Languages : en
Pages : 230
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Book Description
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.
Author: Hai-Jew, Shalin
Publisher: IGI Global
ISBN: 1522533745
Category : Computers
Languages : en
Pages : 346
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Book Description
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.
Author: Stephen Satchell
Publisher: Elsevier
ISBN: 0080471420
Category : Business & Economics
Languages : en
Pages : 428
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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
