Forecasting realized volatility: a review
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Publication:1622112
DOI10.1016/j.jkss.2018.08.002zbMath1406.62129OpenAlexW2889541984WikidataQ129330283 ScholiaQ129330283MaRDI QIDQ1622112
Publication date: 12 November 2018
Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jkss.2018.08.002
asymmetrycovariance matrixmarket microstructure noiselong-memoryrealized variancerealized covarianceHAR modelheterogeneous autoregressive (HAR) model
Inference from stochastic processes and prediction (62M20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Economic time series analysis (91B84)
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Cites Work
- Unnamed Item
- The Model Confidence Set
- Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
- Modelling structural breaks, long memory and stock market volatility: an overview
- A multiple indicators model for volatility using intra-daily data
- Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment
- Predicting volatility: getting the most out of return data sampled at different frequencies
- Out of sample forecasts of quadratic variation
- A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries
- An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: asymptotic distribution theory
- Modelling and forecasting noisy realized volatility
- Estimating the quadratic covariation matrix from noisy observations: local method of moments and efficiency
- The functional central limit theorem and structural change test for the \(\mathrm{HAR}(\infty)\) model
- An integrated heteroscedastic autoregressive model for forecasting realized volatilities
- Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
- Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
- Quasi-maximum likelihood estimation of volatility with high frequency data
- Estimating covariation: Epps effect, microstructure noise
- The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets
- Realized volatility forecasting and market microstructure noise
- Volatility forecasting and microstructure noise
- Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
- Infinite-order, long-memory heterogeneous autoregressive models
- Two-stage stationary bootstrapping for bivariate average realized volatility matrix under market microstructure noise and asynchronicity
- A CUSUMSQ test for structural breaks in error variance for a long memory heterogeneous autoregressive model
- Vast volatility matrix estimation for high-frequency financial data
- Microstructure noise in the continuous case: the pre-averaging approach
- A CUSUM test for a long memory heterogeneous autoregressive model
- Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
- The Benefits of Bagging for Forecast Models of Realized Volatility
- Realized Volatility: A Review
- The Volatility of Realized Volatility
- Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
- Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data
- Realized Volatility
- Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?
- Estimation of structural mean breaks for long-memory data sets
- Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models
- On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model
- Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates
- The Distribution of Realized Exchange Rate Volatility
- High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
- Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities
- A Tale of Two Time Scales
- Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics
- Exploiting the errors: a simple approach for improved volatility forecasting
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