Forecasting Realized Volatility of Russian Stocks Using Google Trends and Implied Volatility PDF Download
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Author: Timofey Bazhenov
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The out-of-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
Author: Timofey Bazhenov
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The out-of-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
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: 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: Vladimir Pyrlik
Publisher:
ISBN: 9788073446154
Category :
Languages : en
Pages :
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Book Description
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: 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: Alexander M. Karminsky
Publisher: Springer Nature
ISBN: 3030697487
Category : Business & Economics
Languages : en
Pages : 395
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Book Description
This book describes various approaches in modelling financial risks and compiling ratings. Focusing on emerging markets, it illustrates how risk assessment is performed and analyses the use of machine learning methods for financial risk assessment and measurement. It not only offers readers insights into the differences between emerging and developed markets, but also helps them understand the development of risk management approaches for banks. Highlighting current problems connected with the evaluation and modelling of financial risks in the banking sector of emerging markets, the book presents the methodologies applied to credit and market financial risks and integrated and payment risks, and discusses the outcomes. In addition it explores the systemic risks and innovations in banking and risk management by analyzing the features of risk measurement in emerging countries. Lastly, it demonstrates the aggregation of approaches to financial risk for emerging financial markets, comparing the experiences of various countries, including Russia, Belarus, China and Brazil.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 630
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Book Description
Author: International Monetary Fund. Monetary and Capital Markets Department
Publisher: International Monetary Fund
ISBN: 1498363288
Category : Business & Economics
Languages : en
Pages : 135
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Book Description
The current Global Financial Stability Report (April 2016) finds that global financial stability risks have risen since the last report in October 2015. The new report finds that the outlook has deteriorated in advanced economies because of heightened uncertainty and setbacks to growth and confidence, while declines in oil and commodity prices and slower growth have kept risks elevated in emerging markets. These developments have tightened financial conditions, reduced risk appetite, raised credit risks, and stymied balance sheet repair. A broad-based policy response is needed to secure financial stability. Advanced economies must deal with crisis legacy issues, emerging markets need to bolster their resilience to global headwinds, and the resilience of market liquidity should be enhanced. The report also examines financial spillovers from emerging market economies and finds that they have risen substantially. This implies that when assessing macro-financial conditions, policymakers may need to increasingly take into account economic developments in emerging market economies. Finally, the report assesses changes in the systemic importance of insurers, finding that across advanced economies the contribution of life insurers to systemic risk has increased in recent years. The results suggest that supervisors and regulators should take a more macroprudential approach to the sector.
Author:
Publisher:
ISBN:
Category : Risk management
Languages : en
Pages : 480
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Book Description
