On copula-based conditional quantile estimators

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

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Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts

Journal of Time Series Econometrics ◽

10.1515/jtse-2019-0021 ◽

2019 ◽

Vol 0 (0) ◽

Author(s):

Jan G. De Gooijer ◽

Dawit Zerom

Keyword(s):

Risk Premium ◽

Empirical Studies ◽

Forecast Accuracy ◽

Equity Premium ◽

Adaptive Lasso ◽

Large Set ◽

Usual Practice ◽

Conditional Quantile ◽

Equity Risk ◽

Non Parametric

Abstract We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor dimension and obtain quantile forecasts. Several recent empirical studies have considered a large set of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile context. The application offers three main findings. First, combining parametric and non-parametric approaches adds quantile forecast accuracy over and above the constituent methods. Second, a handful of macroeconomic predictors are found to have systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper quantiles of the equity premium distribution.

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Measuring the effects of tourists’ relative willingness to spend and third-degree price discrimination on inbound tourism expenditure differentials

Tourism Economics ◽

10.1177/13548166211030016 ◽

2021 ◽

pp. 135481662110300

Author(s):

Usamah F Alfarhan ◽

Khaldoon Nusair ◽

Hamed Al-Azri ◽

Saeed Al-Muharrami ◽

Nan Hua

Keyword(s):

Price Discrimination ◽

Profit Maximization ◽

Tourism Industry ◽

Theoretical Argument ◽

Total Expenditure ◽

Conditional Quantile ◽

For Profit ◽

Inbound Tourism ◽

Tourism Expenditure ◽

Quantile Estimates

Tourism expenditures are determined by a set of antecedents that reflect tourists’ willingness and ability to spend, and de facto incremental monetary outlays at which willingness and ability is transformed into total expenditures. Based on the neoclassical theoretical argument of utility-constrained expenditure minimization, we extend the current literature by applying a sustainability-based segmentation criterion, namely, the Legatum Prosperity IndexTM to the decomposition of a total expenditure differential into tourists’ relative willingness to spend and an upper bound of third-degree price discrimination, using mean-level and conditional quantile estimates. Our results indicate that understanding the price–quantity composition of international inbound tourism expenditure differentials assists agents in the tourism industry in their quest for profit maximization.

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Uncertainty of quantile estimators using the population index flood method

Water Resources Research ◽

10.1029/2002wr001594 ◽

2003 ◽

Vol 39 (8) ◽

Cited By ~ 6

Author(s):

O. G. B. Sveinsson ◽

J. D. Salas ◽

D. C. Boes

Keyword(s):

Population Index ◽

Index Flood ◽

Quantile Estimators

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