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Faculty of Economics

C58

Financial Econometrics


Title AuthorsYearJEL Codes
Do consumption-based asset pricing models explain the dynamics of stock market returns?Ashby, M., Linton, O. B. [2022]C52 C58 G12
A Structural Dynamic Factor Model for Daily Global Stock Market ReturnsLinton, O. B., Tang, H., Wu, J.[2022]C55 C58 G15
Conditional Heteroskedasticity in the Volatility of Asset ReturnsDing, Y. [2021]C22 C32 C53 C58 G17
Augmented Real-Time GARCH: A Joint Model for Returns, Volatility and Volatility of VolatilityDing, Y.[2021]C22 C32 C53 C58
Testing and Modelling Time Series with Time Varying TailsPalumbo, D.[2021]C12 C18 C51 C52 C46 C58 G12
Text-Based Linkages and Local Risk Spillovers in the Equity MarketGe, S.[2020]C33 C58 G10 G12
A Revisit to Sovereign Risk Contagion in Eurozone with Mutual Exciting Regime-Switching ModelGe, S.[2020]C10 C58 F36 G12 G15
Diffusion Limits of Real-Time GARCHDing, Y.[2020]C22 C32 C58
Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency DataLi, Z. M., Laeven, R. J. A. and Vellekoop, M. H.[2019]C13 C14 C55 C58
Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case Hafner, C., Linton, O., Tang, H.[2018]C55 C58 G11
Nonparametric estimation of infinite order regression and its application to the risk-return tradeoffHong, S-Y., Linton, O.[2018]C10 C58 G10
Estimating Nominal Interest Rate Expectations: Overnight Indexed Swaps and the Term StructureLloyd, S. P.[2017]C32 C58 E43 E47 G12
A Semiparametric Intraday GARCH ModelMalec, P.[2016]C14 C22 C53 C58
Asymptotic Theory for Beta-t-GARCHIto, R.[2016]C22 C58
Spline-DCS for Forecasting Trade Volume in High-Frequency FinanceIto, R.[2016]C22 C51 C53 C58 G12
Estimating the Spot Covariation of Asset Prices - Statistical Theory and Empirical EvidenceBibinger, M., Hautsch, N., Malec , P. and Reiss, M.[2014]C58 C14 C32
A multiple testing approach to the regularisation of large sample correlation matricesBailey , N., Smith, V. and Pesaran, H.[2014]C13,C58
Modeling Dynamic Diurnal Patterns in High-Frequency Financial DataIto, R.[2013]C22 C51 C53 C58 G01 G12

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