scholarly journals Evaluating Combined Forecasts for Realized Volatility Using Asymmetric Loss Functions

2018 ◽  
Vol 2 (2) ◽  
pp. 99-111
Author(s):  
Giovanni De Luca ◽  
Giampiero M. Gallo ◽  
Danilo Carità
Keyword(s):  
Realized Volatility ◽  
Loss Functions ◽  
Forecast Combination ◽  
Asymmetric Loss ◽  
Market Indexes ◽  
Combined Forecasts

In this work we provide the findings of a forecast combination analysis carried out on the realized volatility series of three market indexes (DAX, CAC, and AEX). Two volatility types (5 minutes, kernel) have been considered. Different loss functions suggest that forecasts computed through combining models are generally more accurate than those provided by single models. However, the choice of the latter can significantly affect the goodness of the results.

scholarly journals SIMPLE BAYESIAN FORECAST COMBINATION

2020 ◽  
Vol 15 (04) ◽  
pp. 2050016
Author(s):  
PHILIP HANS FRANSES
Keyword(s):  
The Netherlands ◽  
Diffusion Models ◽  
Ease Of Use ◽  
Forecast Combination ◽  
Bayesian Forecast ◽  
Daily Data ◽  
Combined Forecasts

In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.

scholarly journals Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 884-919
Author(s):  
Ulrich Gunter
Keyword(s):  
Moving Average ◽  
Forecast Accuracy ◽  
Forecast Combination ◽  
Autoregressive Integrated Moving Average ◽  
Hotel Room ◽  
Daily Data ◽  
Forecast Models ◽  
Rolling Windows ◽  
Combined Forecasts ◽  
Box Cox Transformation

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

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