On Estimating the Expected Return on the Market: An Exploratory Investigation

By R. C. Merton. 1980

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Citations in the corpus

Threshold-based portfolio: the role of the threshold and its applications (2017, background): “However, it is well known that it is more difficult to estimate the means than the covariances of asset returns [43], and errors in the estimates of means will have a greater impact on portfolio weights than errors in the estimates of covariances.”
It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification (2012, background): “It is well known that we need a long time series of returns to estimate μt accurately (Merton, 1980).”
ECONOMETRIC ANALYSIS OF VOLATILITY COMPONENT MODELS (2014, methodology): “The specification of the τt component for GARCH-MIDAS builds on a long tradition, going back to Merton (1980), Schwert (1989), and others, of measuring long-run volatility by realized volatility over a monthly or quarterly horizon.”
Portfolio Performance Evaluation with Generalized Sharpe Ratios: Beyond the Mean and Variance (2009, unknown): “Some examples are: a class of stable distributions which generalize the normal distribution (see, for example, Samorodnitsky and Taqqu (1994) or Uchaikin and Zolotarev (1999)), the variance gamma (VG) distribution introduced by Madan and Seneta (1990), and the normal inverse Gaussian (NIG) distribut”
International portfolio diversification is better than you think (2011, background): “The historical mean has long been known to be a poor estimator of mean returns (Merton, 1980).”
The risk return tradeoff in the long run: 1836–2003 (2007, unknown): “Turner, Startz, and Nelson (1989), Whitelaw (2000) and Mayfield (2004) employ similar specifications.”
Optimal combinations of realised volatility estimators (2009, background): “Older studies in this “realised volatility” literature, such as French, Schwert, and Stambaugh (1987), Merton (1980), and Zhou (1996), recognised the benefits from such an approach in increased accuracy, and recent work1 has built on this to propose estimators that are more efficient, are robust to…”
Nonparametric specification testing for continuous-time models with application to spot interest rates (2002, unknown): “This is particularly relevant in the present context because it is well-known that the drift parameters are difficult to estimate (e.g., Merton 1980).”
Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk (2006, methodology): “We use daily data in order to improve the estimation precision (see Merton (1980)), and we time aggregate to a 7 monthly frequency for our cross sectional analysis.6 The value-weighted (cum-dividend) Center for Research in Security Prices (CRSP) portfolio return is our measure of the market return,…”
Stock Market Risk and Return: An Equilibrium Approach (2000, background): “Put slightly differently, regime shifts introduce large movements in the investment opportunity set, and therefore induce a desire among investor’s to hedge adverse changes [see Merton (1973)].”
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach (2004, unknown): “We focus on the error in estimating expected returns of assets because as shown by Merton (1980) they are much harder to estimate than the variances and covariances.”
Does Idiosyncratic Volatility Proxy for Risk Exposure (2012, background): “In general, we would expect that when average variance goes up overall market variance increases and drives up the expected market risk premium (Merton 1980).”
Volatility forecasting of crude oil market: A new hybrid method (2018, background): “The squared daily return is taken as the index for actual volatility (Li, Huang, & Zhang, 2013; Merton, 1980).”