Narrow search
Showing 1 - 10 of 23,892
Cover Image
Bayesian inference for latent factor copulas and application to financial risk forecasting
Schamberger, Benedikt; Gruber, Lutz F.; Czado, Claudia - In: Econometrics : open access journal 5 (2017) 2, pp. 1-23
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use...
Persistent link: https://www.econbiz.de/10011654443
Cover Image
Cryptocurrencies : a copula based approach for asymmetric risk marginal allocations
Jeleskovic, Vahidin; Meloni, Mirko; Younas, Zahid Irshad - 2020
, 1h) are included in the estimation of univariate GARCH models, to be used in combination with copula functions for VaR …
Persistent link: https://www.econbiz.de/10012542685
Cover Image
Copula-based factor models for multivariate asset returns
Ivanov, Eugen; Min, Aleksey; Ramsauer, Franz - In: Econometrics : open access journal 5 (2017) 2, pp. 1-24
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the...
Persistent link: https://www.econbiz.de/10011654435
Cover Image
Estimation Risk and Shrinkage in Vast-Dimensional Fundamental Factor Models
van Vlodrop, Andries - 2019
We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental … about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help … to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are …
Persistent link: https://www.econbiz.de/10012896346
Cover Image
Estimation risk and shrinkage in vast-dimensional fundamental factor models
Vlodrop, Andries C. van; Lucas, André - 2018
We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental … about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help … to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are …
Persistent link: https://www.econbiz.de/10011949129
Cover Image
Option-Implied Correlations, Factor Models, and Market Risk
Buss, Adrian - 2017
Implied correlation and variance risk premium stand out in predicting market returns. However, while the predictive ability of implied correlation lasts for up to a year, the variance risk premium predicts market returns only for one quarter ahead. Contrary to the accepted view, implied...
Persistent link: https://www.econbiz.de/10012964588
Cover Image
Time-Varying Semi-Nonparametric Distribution and Portfolio Performance
Leon, Angel - 2018
In this paper, we extend the semi-nonparametric (SNP) densities of León, Mencía and Sentana (2009) through time-varying (TV) volatility, skewness and kurtosis. We derive some parametric properties, the conditional expected shortfall, quantiles and partial moments. We obtain closed-form...
Persistent link: https://www.econbiz.de/10012914377
Cover Image
On the predictability of stock prices : a case for high and low prices
Caporin, Massimiliano; Ranaldo, Angelo; Santucci de … - 2012
Contrary to the common wisdom that asset prices are barely possible to forecast, we show that that high and low prices of equity shares are largely predictable. We propose to model them using a simple implementation of a fractional vector autoregressive model with error correction (FVECM). This...
Persistent link: https://www.econbiz.de/10010407671
Cover Image
Exponential high-frequency-based-volatility (EHEAVY) models
Xu, Yongdeng - 2022
. A joint quasi-maximum likelihood estimation and closed form multi-step ahead forecasting is derived. The model is …
Persistent link: https://www.econbiz.de/10013177995
Cover Image
Forecasting Stock Market Volatility and Application to Volatility Timing Portfolios
Chun, Dohyun; Cho, Hoon; Ryu, Doojin - 2022
This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...
Persistent link: https://www.econbiz.de/10013404229