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Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector

https://doi.org/10.2139/ssrn.4747889
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Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction
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Volatility analysis with realized GARCH-Itô models
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Bias reduction in spot volatility estimation from options
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Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
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Introduction to the non-asymptotic analysis of random matrices
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NONPARAMETRIC FILTERING OF THE REALIZED SPOT VOLATILITY: A KERNEL-BASED APPROACH
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KERNEL ESTIMATION OF SPOT VOLATILITY WITH MICROSTRUCTURE NOISE USING PRE-AVERAGING
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Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model
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Time-Varying Periodicity in Intraday Volatility
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Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data
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Volatility Forecasting with Machine Learning and Intraday Commonality
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A Simple Approximate Long-Memory Model of Realized Volatility
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Forecasting intraday volatility in the US equity market. Multiplicative component GARCH
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Matrix Completion With Noise
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Trading and Returns under Periodic Market Closures
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Determining the Number of Factors in Approximate Factor Models
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Modeling and Forecasting Realized Volatility
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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XGBoost
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Determining the Number of Factors from Empirical Distribution of Eigenvalues
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Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data
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Comparing Predictive Accuracy
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CAViaR
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High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data
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Estimating the quadratic covariation matrix from noisy observations: Local method of moments and efficiency
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Asymptotics of empirical eigenstructure for high dimensional spiked covariance
inferredcites →https://doi.org/10.1214/17-aos1588openalex-extracted
Large covariance estimation through elliptical factor models
inferredcites →https://doi.org/10.1214/19-aap1488openalex-extracted
Nonparametric spot volatility from options
inferredcites →https://doi.org/10.2139/ssrn.2315801openalex-extracted
Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency
inferredcites →https://doi.org/10.2139/ssrn.4022147openalex-extracted
Volatility Forecasting with Machine Learning and Intraday Commonality
inferredcites →https://doi.org/10.2139/ssrn.4184933openalex-extracted
Robust Estimation of Integrated and Spot Volatility
inferredcites →https://doi.org/10.2139/ssrn.619682openalex-extracted
Efficient Estimation of Stochastic Volatility Using Noisy Observations: A Multi-Scale Approach
inferredcites →https://doi.org/10.2307/2171927openalex-extracted
Continuous Record Asymptotics for Rolling Sample Variance Estimators
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Evaluating Interval Forecasts
inferredcites →https://doi.org/10.3150/bj/1165269149openalex-extracted
Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
inferredcites →https://doi.org/10.3386/t0163openalex-extracted
Continuous Record Asymptotics for Rolling Sample Variance Estimators
inferredcites →https://doi.org/10.3386/w13825openalex-extracted
High Frequency Market Microstructure Noise Estimates and Liquidity Measures
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Techniques for Verifying the Accuracy of Risk Measurement Models
inferredcites →https://doi.org/10.3982/ecta6495openalex-extracted
Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
inferredcites →https://doi.org/10.3982/ecta8968openalex-extracted
Eigenvalue Ratio Test for the Number of Factors
inferredcites →https://doi.org/10.4310/sii.2008.v1.n2.a5openalex-extracted
Spot volatility estimation for high-frequency data
inferredcites →https://doi.org/10.48550/arxiv.2102.12752openalex-extracted
Adaptive Robust Large Volatility Matrix Estimation Based on High-Frequency Financial Data
inferredcites →https://doi.org/10.5705/ss.202016.0205openalex-extracted
Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise
inferredcites →https://doi.org/10.48550/arxiv.1603.02754openalex-snapshot
XGBoost: A Scalable Tree Boosting System
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