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Bivariate volatility modeling with high-frequency data

Matei, Marius; Rovira, Xari; Agell, Núria - In: Econometrics : open access journal 7 (2019) 3/41, pp. 1-15

volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate …

Persistent link: https://www.econbiz.de/10012160811

Media attention vs. sentiment as drivers of conditional volatility predictions : an application to Brexit

Guidolin, Massimo; Pedio, Manuela - 2020

coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of …

Persistent link: https://www.econbiz.de/10012487265

Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

Persistent link: https://www.econbiz.de/10012814196

Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market

Götze, Tobias; Gürtler, Marc; Witowski, Eileen - In: Journal of Business Economics 93 (2023) 9, pp. 1629-1660

The main challenge in empirical asset pricing is forecasting the future value of assets traded in financial markets … have shown promising forecasting results and significantly outperform traditional regression methods. Corresponding results … this background, this study aims to build the first out-of-sample forecasting model for CAT bond premia in the secondary …

Persistent link: https://www.econbiz.de/10015272797

Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data

Marchese, Malvina; Martinez Miranda, Maria Dolores; … - In: Financial innovation : FIN 10 (2024), pp. 1-16

The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new...

Persistent link: https://www.econbiz.de/10015361553

Predicting the global minimum variance portfolio

Reh, Laura; Krüger, Fabian; Liesenfeld, Roman - 2020

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

Persistent link: https://www.econbiz.de/10012250683

Predictive inference for integrated volatility

Corradi, Valentina; Distaso, Walter; Swanson, Norman R. - 2011

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned...

Persistent link: https://www.econbiz.de/10010282869

Volatility forecasting: the jumps do matter

Corsi, Fulvio; Pirino, Davide; Renò, Roberto - Dipartimento di Economia Politica e Statistics, … - 2008

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have positive and mostly … estimator provides less biased and robust estimates of the continuous quadratic variation and jumps. This technique also …

Persistent link: https://www.econbiz.de/10005766526

On the Interaction between Ultra–high Frequency Measures of Volatility

Gallo, Giampiero; Velucchi, Margherita - Dipartimento di Statistica, Informatica, Applicazioni … - 2007

We analyze several measures of volatility (realized variance, bipower variation and squared daily returns) as estimators of integrated variance of a continuous time stochastic process for an asset price. We use a Multiplicative Error Model to describe the evolution of each measure as the product...

Persistent link: https://www.econbiz.de/10005812866

Volatility Forecasting: The Jumps Do Matter

Corsi, Fulvio; Pirino, Davide; Reno, Roberto - Institute of Economic Research, Hitotsubashi University - 2009

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly … estimator provides less biased and robust estimates of the continuous quadratic variation and jumps. This technique also …

Persistent link: https://www.econbiz.de/10005784004