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Robust Bootstrap Densities for Dynamic Conditional Correlations : Implications for Portfolio Selection and Value-at-Risk
Trucíos, Carlos - 2017
these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation (DCC) models when … for return, volatilities, conditional correlation and VaR that is robust to outliers. The results are illustrated with …
Persistent link: https://www.econbiz.de/10012956168
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On the economic determinants of optimal stock-bond portfolios : international evidence
Conrad, Christian; Stürmer, Karin - 2017 - This version: May 31, 2017
Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple … explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by … portfolios in terms of portfolio risk. While optimal daily weights minimize portfolio risk, we find that portfolio turnover and …
Persistent link: https://www.econbiz.de/10011745369
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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2022 - This version: January 2022
unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC …
Persistent link: https://www.econbiz.de/10013040932
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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2021 - This version: June 2021
unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC …
Persistent link: https://www.econbiz.de/10012584099
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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2020
. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this …
Persistent link: https://www.econbiz.de/10012253083
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Exogenous Shocks, Dynamic Correlations, and Portfolio Risk Management for the Asian Emerging and Other Global Developed and Emerging Stock Markets
Dong, Xiyong; Li, Changhong; Yoon, Seong-Min - 2023
This study examines the potential influence of exogenous shocks on time-varying correlations and portfolio strategies between the Asian emerging and other global stock markets including developed and other emerging markets. Using the ARMA-cDCC-FIEGARCH model with and without exogenous shocks,...
Persistent link: https://www.econbiz.de/10014351309
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Correlations and volatility spillover from China to Asian and Latin American Countries : identifying diversification and hedging opportunities
Yadav, Miklesh Prasad; Sharma, Sudhi; Aggarwal, Vaibhav; … - In: Cogent economics & finance 10 (2022) 1, pp. 1-21
, Mexico, and Brazil. For empirical purpose, the study employs VARMA-Multivariate Generalized Autoregressive Conditional … Heteroskedasticity (MGARCH) model with BEKK, diagonal, Constant Conditional Correlation (CCC), and finally, Dynamic Conditional … Correlation (DCC) specifications. DCC model outperforms among others and identifies two diversification opportunities with Mexican …
Persistent link: https://www.econbiz.de/10014500295
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Large Covariance Estimation by Bootstrapped Hierarchies and Shrinkage
Bongiorno, Christian; Tan, Vincent; Zohren, Stefan - 2022
This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress...
Persistent link: https://www.econbiz.de/10014239116
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Liquidity and Algorithmic Trading in Brazil
Ramos, Henrique - 2019
This paper provides evidence of the effect of algorithmic trading (AT) in the liquidity of the Brazilian equity market …
Persistent link: https://www.econbiz.de/10012867372
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Ambiguity with Machine Learning : An Application to Portfolio Choice
Ghysels, Eric; Qian, Yan; Raymond, Steve - 2021
To characterize ambiguity we use machine learning to impose guidance and discipline on the formulation of expectations in a data-rich environment. In addition, we use the bootstrap to generate plausible synthetic samples of data not seen in historical real data to create statistics of interest...
Persistent link: https://www.econbiz.de/10013322742