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Is intraday data useful for forecasting VaR? : the evidence from EUR/PLN exchange rate

Będowska-Sójka, Barbara - In: Risk management : a journal of risk, crisis and disaster 20 (2018) 4, pp. 326-346

Persistent link: https://www.econbiz.de/10011962183

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...

Persistent link: https://www.econbiz.de/10009771770

Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance

Asai, Manabu; McAleer, Michael - 2014

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory....

Persistent link: https://www.econbiz.de/10010259630

Estimating the volatility of asset pricing factors

Becker, Janis; Leschinski, Christian - 2018 - This version: May 20, 2018

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

Persistent link: https://www.econbiz.de/10011860248

High frequency vs. daily resolution : the economic value of forecasting volatility models 2nd ed

Lilla, Francesca - 2017

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing … fat-tails) and leverage effects translates in more accurate VaR measure. …

Persistent link: https://www.econbiz.de/10011674479

High frequency vs. daily resolution : the economic value of forecasting volatility models

Lilla, Francesca - 2016

forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping … prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S … accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR …

Persistent link: https://www.econbiz.de/10011730304

A SHARP Model of Bid-Ask Spread Forecasts

Cattivelli, Luca - 2019

This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a...

Persistent link: https://www.econbiz.de/10012902435

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional...

Persistent link: https://www.econbiz.de/10014252427

Modeling and forecasting multifractal volatility established upon the heterogeneous market hypothesis

Tao, Qizhi; Wei, Yu; Liu, Jiapeng; Zhang, Ting - In: International review of economics & finance : IREF 54 (2018), pp. 143-153

Persistent link: https://www.econbiz.de/10012033354

Multi-period credit default prediction with time-varying covariates

Orth, Walter - 2011 - First draft: March 17, 2011

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock...

Persistent link: https://www.econbiz.de/10008939079

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