A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns

By James W. Taylor. 1999

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Citations in the corpus

Distributional Reinforcement Learning with Quantile Regression (2017, methodology): “These methods have been used in a variety of settings where outcomes have intrinsic randomness (Koenker and Hallock 2001); from food expenditure as a function of household income (Engel 1857), to studying value-at-risk in economic models (Taylor 1999).”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, background): “As is common in the VaR literature, Taylor (1999) judges the efficiency of VaR estimates only on the basis of unconditional coverage (to be defined in Section 2 below).”
Quantile regression: applications and current research areas (2003, methodology): “Using data on 950 daily observations of the exchange rates for the British pound, German Deutschmark and Japanese yen quoted against the US dollar between June 25th, 1990, and March 23rd, 1994, Taylor (1999) employed a quantile regression approach for the distribution of multiperiod returns.”
Quantile cointegrating regression (2009, methodology): “Taylor (1999) applies quantile regression approach to estimating the distribution of multiperiod returns.”
A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns (2000, methodology): “This criterion is employed by Alexander and Leigh (1997) in a value at risk study, and is used extensively for evaluating quantile estimators and prediction intervals (e.g. Granger et al., 1989; Taylor and Bunn, 1999; Taylor, 1999b).”
Estimating Structural Changes in Regression Quantiles (2011, background): “Example 2 Chernozhukov and Umantsev (2001) studied the following model for Value-at-Risk:1 Qyt( jxt) = x0t ( ); (3) 1See Taylor (1999) and Engle and Manganelli (2004) for related works.”
Quantile Regression Estimator for GARCH Models (2013, methodology): “…U.S. wage structure by using different estimated quantiles and investigated the factors affecting national economic growth in different countries; Taylor (1999), Chernozhukov & Umantsev (2001), and Engle & Manganelli (2004), who used quantile regression to study the value at risk (VaR) of asset…”
An introduction to market risk measurement (2002, background): “CrashMetrics is a form of maximum loss optimisation that is designed to estimate worst-case losses (see Hua and Wilmott (1997) or Wilmott (2000, ch.”