Variable Selection for Portfolio Choice

By Yacine Aït-Sahalia, Michael W. Brandt. 2001

409 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3865e424bbfc1a9fea3f76da071dd512d38cab7c

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Citations in the corpus

Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns (2005, neutral): “Ait-Sahalia and Brandt (2001) document a similar non-monotonic effect of the coefficient of risk aversion when predictability originates from a composite index of economic variables.”
Asset Allocation Under Multivariate Regime Switching (2005, positive): “We also found slightly non-monotonic investment schedules for stocks under the VAR(1) model, which is not completely surprising.”
Does the Failure of the Expectations Hypothesis Matter for Long-Term Investors (2005, positive): “We could examine the relative importance of these state variables, as in Ait-Sahalia and Brandt (2001).”
On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation (2004, neutral): “By ensuring that our estimate always lies in the interval [0, 1] we employ a type of insanity Þlter which prevents the investor from taking an extreme position in the market.”
Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation (2007, positive): “(9)Although beyond the scope of this paper, the kinked utility function, used by Benartzi and Thaler (1995) and Aı̈t-Sahalia and Brandt (2001), seems another important class of preference for analyzing asymmetries.”
Portfolio Choice Problems (2010, neutral): “These non-expected utility preferences have been applied to portfolio choice problems by Benartzi and Thaler (1995), Shefrin and Statman (2000), Aı̈t-Sahalia and Brandt (2001), Liu (2002b), Ang, Bekaert, and Liu (2003), and Gomes (2003), among others. Finally, there are numerous applications of more”
Stochastic Partial Differential Equations and Portfolio Choice (2010, neutral): “The predictability of stock returns was rst discussed in [13], [14] and [16] (see also [1], [8], [42] and others).”
VOLATILITY AND CORRELATION FORECASTING (2006, positive): “Campbell (1987, 2003), Shanken (1990), Aït-Sahalia and Brandt (2001), Harvey (2001), Lettau and Ludvigsson (2003) and Marquering and Verbeek (2004) find that interest rate spreads and financial ratios help predict volatility at longer horizons.”
Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets (2005, positive): “However, Campbell (1987), Harvey (1989, 1991), Glosten, Jagannathan, and Runkle (1993), Aı̈t-Sahalia and Brandt (2001), and others have modeled time-varying expected returns and volatility jointly and found that the effects of state variables on expected returns are stronger than their effects on vo”
Predictability of stock returns and asset allocation under structural breaks (2011, neutral): “Campbell (1987), Fama and French (1988), Kandel and Stambaugh (1996), Aït-Sahalia and Brandt (2001), Pesaran and Timmermann (1995). (2)See, e.”
Optimal Portfolio Allocation under Higher Moments (2006, neutral): “Although a large number of tests have been proposed in the literature, we focus on three well-known procedures, that have proved to be relevant in similar contexts: (1) the statistic (JB) proposed by Jarque and Bera (1980) tests whether skewness and excess kurtosis are jointly zero, using the asympt”
Investing in Mutual Funds when Returns are Predictable (2006, positive): “Nevertheless, for a wide variety of preferences, hedging demands are small, or even nonexistent, as demonstrated by Ait-Sahalia and Brandt (2001), among others. Indeed, in unreported tests, we have explicitly derived the hedging demands, following Fama’s (1996) intuition about Merton’s ICAPM.”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, positive): “For example, Mykland and Zhang (2006) develop tools to conduct analysis of variance as well as univariate regression, while Todorov and Bollerslev (2010) add a jump component in the univariate regression setting and Aït-Sahalia et al. (2014) extend the factor model further to allow for multivariate ”
Optimal Portfolio Choice with Parameter Uncertainty (2007, positive): “For example, it is of interest to ex tend our analysis to more complex dynamic portfolio choice problems, such as the setups of Barberis (2000) and Ait-Sahalia and Brandt (2001). In fact, economi cally better estimates can potentially be sought in many financial decisions, either in investments or i”
On the Relationship between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach (2004, positive): “(3)An alternative to estimating the two conditional moments separately and then relating the estimates is to infer directly the conditional mean-variance ratio from the first-order conditions (Euler equations) of a mean-variance investor’s portfolio choice (e.g., Brandt, 1999; Aı̈t-Sahalia and Brand”