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Does real exchange rate volatility affect sectoral trade flows?

Caglayan, Mustafa; Jing Di - In: Southern economic journal 77 (2010) 2, pp. 313-335

Persistent link: https://www.econbiz.de/10008732196

Assessing the Accuracy of Exponentially Weighted Moving Average Models for Value-at-Risk and Expected Shortfall of Crypto Portfolios

Alexander, Carol; Dakos, Michael - 2022

provide extensive backtests of hourly and daily Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts that are regarded as … model's variance and covariance forecasts using average scores generated from proper univariate and multivariate scoring … rules, there is no evidence of superior performance of variance and covariance forecasts generated by GARCH models, using …

Persistent link: https://www.econbiz.de/10013292091

Internet Appendix to `Forecasting Realized Volatility of the Oil Future Prices via Machine Learning'

Kim, Byung-June; Kim, Taeyoon; Jang, Bong-Gyu - 2021

The Internet Appendix consists of three sections. Section A shows data sources and detailed data processing procedures. In Section B, we outline seven forecasting models. Last, Section C represents the empirical results

Persistent link: https://www.econbiz.de/10013241114

Forecasting Realized Volatility of the Oil Future Prices Via Machine Learning

Kim, Byung-June; Kim, Taeyoon; Jang, Bong-Gyu - 2021

This paper explores the possibility of the potential usage of machine learning models in the field of realized volatility forecasting of crude oil with a vast variety of empirical analyses and robustness checks. Although the conventional heterogeneous autoregressive (HAR) model is widely...

Persistent link: https://www.econbiz.de/10013241115

Volatility Forecasting Accuracy for Bitcoin

Köchling, Gerrit - 2020

We analyze the quality of Bitcoin volatility forecasting of GARCH-type models applying different volatility proxies and loss functions. We construct model confidence sets and find them to be systematically smaller for asymmetric loss functions and a jump robust proxy

Persistent link: https://www.econbiz.de/10012826448

Regime changes in Bitcoin GARCH volatility dynamics

Ardia, David - 2019

-day ahead Value-at-Risk (VaR). The Bayesian approach is used to estimate the model parameters and to compute the VaR forecasts …

Persistent link: https://www.econbiz.de/10012899272

A nonparametric approach to forecasting realized volatility

Clements, Adam; Becker, Ralf - National Centre for Econometric Research (NCER) - 2009

application of a multivariate kernel scheme. It is found that at a 1 day forecast horizon, the proposed method produces forecasts …

Persistent link: https://www.econbiz.de/10005036160

GFC-Robust Risk Management Strategies under the Basel Accord

McAleer, Michael - 2010

-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point … VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that … 2008-09 global financial crisis. We investigate the performance of a variety of single and combined VaR forecasts in terms …

Persistent link: https://www.econbiz.de/10013137384

Realized Volatility Risk

Allen, David E. - 2010

In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even...

Persistent link: https://www.econbiz.de/10013149893

Modelling and Forecasting Noisy Realized Volatility

Asai, Manabu - 2009

-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large; and … (iii) even the partially corrected recently proposed in the literature should be fully corrected for evaluating forecasts …

Persistent link: https://www.econbiz.de/10013156240