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Improving Portfolio Allocation Through Covariance Matrix Filtering

Golden, Daron - 2017

The sample covariance matrix is known to contain substantial statistical noise, making it inappropriate for use in financial decision making. Leading researchers have proposed various filtering methods that attempt to reduce the level of noise in the covariance matrix estimator. In most cases,...

Persistent link: https://www.econbiz.de/10012965654

Apparent criticality and calibration issues in the Hawkes self-excited point process model : application to high-frequency financial data

Filimonov, Vladimir; Sornette, Didier - 2013

We present a careful analysis of possible issues of the application of the self-excited Hawkes process to high …-frequency financial data, in particular: (i) the impact of overnight trading in the analysis of long-term trends, (ii) intraday … seasonality and detrending of the data and (ii) vulnerability of the analysis to day-to-day nonstationarity and regime shifts …

Persistent link: https://www.econbiz.de/10010257507

Black-Litterman in Continuous Time : The Case for Filtering

Davis, Mark - 2013

In this article, we extend the Black-Litterman approach to a continuous time setting. We model analyst views jointly with asset prices to estimate the unobservable factors driving asset returns. The key in our approach is that the filtering problem and the stochastic control problem are...

Persistent link: https://www.econbiz.de/10013082305

Strategic Asset Allocation Considerations for German Pension Insurance Funds : Theoretical Analysis and Empirical Evidence - Applying Stochastic Time Series Simulations and Dynamic...

Hertrich, Christian - 2011

portfolio allocations for varying asset classes and investment strategies. The empirical methodology applied in our analysis …

Persistent link: https://www.econbiz.de/10013120648

Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models

Carvalho, Carlos M. - 2018

We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and...

Persistent link: https://www.econbiz.de/10012910552

Profitability of time series momentum

He, Xue-zhong; Li, Kai - In: Journal of banking & finance 53 (2015), pp. 140-157

Persistent link: https://www.econbiz.de/10011377714

Dynamic Volatility Management : From Conditional Volatility to Realized Volatility

Zhang, Rongju - 2019

The volatility of concern in conventional volatility-managed strategies such as volatility-targeting strategy and mean-variance optimization is the expected conditional volatility. However for investors, it is the realized volatility that is important, because there is only one realization in...

Persistent link: https://www.econbiz.de/10012890272

Large Dimensional Time-Varying GMM Estimation : A New Approach

Cui, Liyuan; Hong, Yongmiao; Wang, Junhui - 2021

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

Persistent link: https://www.econbiz.de/10013234588

Unrestricted Maximum Likelihood Estimation of Multivariate Realized Volatility Models

Vogler, Jan; Golosnoy, Vasyl - 2022

The popular conditional autoregressive Wishart (CAW) model for dynamics of realized covariance matrices provides a flexible parametrisation. However, the number of parameters grows quadratically with the number of assets, which causes enormous computational difficulties in higher dimensions....

Persistent link: https://www.econbiz.de/10013292096

Filtering Noise from Correlation Matrices : Paper 3 in a Series 'Uncovering Hidden Dependencies'

Izmailov, Alexander - 2014

Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C., present in a series of white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the New York...

Persistent link: https://www.econbiz.de/10013062134

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