Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market) PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market) PDF full book. Access full book title Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market) by Vladimir Pyrlik. Download full books in PDF and EPUB format.
Author: Vladimir Pyrlik
Publisher:
ISBN: 9788073446154
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Vladimir Pyrlik
Publisher:
ISBN: 9788073446154
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Xiaolin Wang
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 0
Get Book Here
Book Description
Volatility modeling and forecasting are crucial in risk management and pricing derivatives. High-frequency financial data are dynamic and affected by the microstructure noise. For the univariate case, we define the two-scale realized volatility estimator as the measure of the volatility of high-frequency financial data. Two main models for volatility, Generalized Autoregressive Conditional Heteroscedastic (GARCH) and Heterogeneous Autoregressive (HAR), are evaluated and compared for the realized volatility forecast of four major stock indices high-frequency data. We also consider the measures of jump component and heteroskedasticity of the error in the extended HAR models. For the improvement of forecasting accuracy of realized volatility, this dissertation develops hybrid forecasting models combining the GARCH and HAR family models with the machine learning methods, Support Vector Regression(SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Transformer. We construct hybrid models using the outputs of the GARCH and HAR family models. In the empirical application, we demonstrate improvements of the hybrid models for one-day ahead realized volatility forecast accuracy. The results show that the hybrid LSTM and Transformer based models provide more accurate forecasts than the other models. In the financial markets, it is well accepted that the volatilities are time-varying correlated across the indices. We construct two portfolios, the Index portfolio and the Forex portfolio. The Index portfolio contains three major stock indices, and the Forex portfolio includes three major exchange rates. We model the conditional covariances of the two portfolios with BEKK, DCC-GARCH, and Vector HAR. The hybrid models combine the estimations of traditional multivariate models and the machine learning framework. Results of the study indicate that for one-day ahead volatility matrix forecasting, these hybrid models can achieve better performance than the traditional models for the two portfolios.
Author: Dean Fantazzini
Publisher: Litres
ISBN: 5042017135
Category : Computers
Languages : en
Pages : 27
Get Book Here
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: Timofey Bazhenov
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
Get Book Here
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: Roel C. A. Oomen
Publisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 48
Get Book Here
Book Description
Author: Rafael Branco
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional predictors (composed by lagged returns and macroeconomic variables), and compare them to the widely used heterogeneous autoregressive (HAR) model. Our main conclusions are that (i) the additional predictors improve the out-of-sample forecasts of 1-day-ahead RV, and (ii) we find no evidence that nonlinear models can outperform statistically either linear models or HAR models with additional predictors.
Author: Renuka Sharma
Publisher: John Wiley & Sons
ISBN: 1394214316
Category : Computers
Languages : en
Pages : 358
Get Book Here
Book Description
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Author: Constantin Roth
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.
Author: Sergio Andrés González Orjuela
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Volatility trading has become a prominent alternative to the traditional stock trading as the rapid development of web-trading in recent years has reduced significantly the costs of operating in the market. Moreover, machine learning techniques have enabled traders to rely heavily on statistical decision-making models to enhance the commonly used technical analysis. In this paper, a machine learning approach is used to predict proxies of short-term implied volatility clusters with high-frequency data, in order to perform trading strategies using vanilla options on a commercial platform. The empirical results indicate that tree-based methods outperform linear models in classifying these clusters using the time of the day as a key variable in the forecasting task. Financial results were mixed due to the high costs of operating in a 5-hour horizon, but it was found that long positions on at the money straddle strategies expiring in one day were profitable. The framework developed here can be used by small investors as a guidance to implement and assess theoretical strategies in accessible markets.
Author: Linkai Huang
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
When forecasting stock market volatility with a standard volatility method (GARCH), it is common that the forecast evaluation criteria often suggests that the realized volatility (the sum of squared high-frequency returns) has a better prediction performance compared to the historical volatility (extracted from the close-to-close return). Since many extensions of the GARCH model have been developed, we follow the previous works to compare the historical volatility with many new GARCH family models (i.e., EGARCH, TGARCH, and APARCH model) and realized volatility with the ARMA model. Our analysis is based on the S&P 500 index from August 1st, 2018 to February 1st, 2019 (127 trading days), and the data has been separated into an estimation period (90 trading days) and an evaluation period (37 trading days). In the evaluation period, by taking realized volatility as the proxy of the true volatility, our empirical result shows that the realized volatility with ARMA model provides more accurate predictions, compared to the historical volatility with the GARCH family models.
