On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model

By J. Vu. 2000

591 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/be79dac10a75ae071ed734880c69902d09d55619

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Citations in the corpus

Stock Return Serial Dependence and Out-of-Sample Portfolio Performance (2014, background): “…a large part of the literature on portfolio selection has focussed on improving the estimation of the covariance matrix: see, for example, Chan, Karceski, and Lakonishok (1999), Ledoit and Wolf (2003, 2004), DeMiguel and Nogales (2009), and DeMiguel, Garlappi, Nogales, and Uppal (2009).”
Semi-Parametric Modelling of Correlation Dynamics (2005, background): “Chan et al. (1999) suggest that any differences between the different covariance models may be brought to light more clearly when the dominant factor is removed.”
Evaluating Covariance Matrix Forecasts in a Value-at-Risk Framework (2000, unknown): “The test for correct conditional coverage developed by Christoffersen (1998) and multinomial extensions of these tests, which can examine several VaR estimates at the same time, are also used to examine these VaR estimates.”
The 1/ N investment strategy is optimal under high model ambiguity (2012, background): “Chan et al. (1999); Jagannathan and Ma (2003) conduct similar studies and also conclude that it is hard to find an investment policy that consistently outperforms the uniform investment strategy.”
High-dimensional minimum variance portfolio estimation based on high-frequency data (2020, unknown): “Empirical studies in Haugen and Baker (1991), Chan et al. (1999), Schwartz (2000), Jagannathan and Ma (2003) and Clarke et al. (2006) have found that the MVP enjoys both lower risks and higher returns compared with some benchmark portfolios.”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “This approach was implemented in the computer programme DAVINS (Direct Analysis of Very Intricate NMR Spectra) [7], and was reasonably successful.”
An Analysis of Covariance Risk and Pricing Anomalies (2003, unknown): “In addition, the motivation in Chan, Karceski, and Lakonishok (1999) is different from that here.”
Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection (2010, methodology): “For our empirical testing of the out-of-sample performance of the improved estimators for higherorder moments and comoments, we mostly follow the empirical methodology adopted by Chan, Karceski, and Lakonishok (1999) and Jagannathan and Ma (2003).”
Noise sensitivity of portfolio selection under various risk measures (2006, background): “…obey normal or Gaussian statistics, and discusses the noise sensitivity of the natural risk measure associated with Gaussian portfolios, namely, the variance (e.g. Frankfurter et al., 1971; Dickinson, 1974; Jobson and Korkie, 1980; Elton and Gruber, 1973; Eun and Resnick, 1984; Chan et al., 1999).”
Covariance Regression Analysis (2017, unknown): “In addition, many researchers have shown that such a covariance matrix is affected by firms’ fundamentals (Roll 1988; Chan, Karceski, and Lakonishok 1998, 1999).”
Improving Portfolio Selection Using Option-Implied Volatility and Skewness (2013, unknown): “2 These approaches include: imposing a factor structure on returns ( Chan, Karceski, and Lakonishok, 1999;”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “They are also popular in the finance literature (e.g., Chan, Karceski, and Lakonishok 1999 and Briner and Connor 2008), where imposing a factor structure is often found to improve covariance forecasts in portfolio optimization.”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “The use of minimum tracking error portfolios is motivated by the analysis in Chan et al. (1999) who demonstrate that based on minimum variance portfolios it is difficult to distinguish between different covariance matrix estimates in the presence of a dominant (market) factor. Eliminating the domina”