scholarly journals Estimating the Level of the Brazilian Yield Curve Using the Time-Varying Coefficient Model GAS (2,2) with Gamma Distribution

2019 ◽  
Vol 11 (9) ◽  
pp. 1
Author(s):  
Daiane Rodrigues dos Santos ◽  
Tiago Costa Ribeiro ◽  
Marco Aurélio Sanfins
Keyword(s):  
Time Series ◽  
Gamma Distribution ◽  
Yield Curve ◽  
Developing Economies ◽  
Evaluation Criteria ◽  
Score Function ◽  
Predictive Distribution ◽  
Time Varying ◽  
Varying Coefficient ◽  
Time Series Modelling

The level of the yield curve is strongly associated with a very important macroeconomic variable for developing economies: the inflation. Therefore, it becomes relevant for economic studies the development of a time series model that can accurately predict this variable. This article proposes the estimation and prediction of the yield curve level using the GAS (Generalized Autoregressive Score) class of time-varying coefficient models. The formulation of these models facilitates a general framework for time series modelling presenting a series of advantages, including the possibility of specifying any conditional distribution deemed appropriate for the yield curve level. In addition, the complete structure of the predictive distribution is transported to the mechanism that updates the time-varying parameters, via score function. When analyzing the evaluation criteria, the measures of adherence, and both Wilcoxon and Diebold & Mariano tests, it was verified that the adjustment of the GAS model (2,2) with gamma distribution to the series containing the Brazilian Yield Curve level of January 2006 and February 2017 presented a satisfactory result.

scholarly journals Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xingcai Zhou ◽  
Fangxia Zhu
Keyword(s):  
Time Series ◽  
Asymptotic Properties ◽  
Time Series Models ◽  
Time Varying ◽  
Bahadur Representation ◽  
Conditional Quantile ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
M Estimation ◽  
Time Varying Coefficients

This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

scholarly journals TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS

2010 ◽  
Vol 11 (3) ◽  
pp. 511-532 ◽  
Author(s):  
Bora Aktan ◽  
Renata Korsakienė ◽  
Rasa Smaliukienė
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Garch Model ◽  
Large Family ◽  
Time Varying ◽  
Increased Risk ◽  
Time Series Modelling ◽  
International Investors ◽  
The Baltic ◽  
Daily Returns

As time‐varying volatility has found applications in roughly all time series modelling in economics, it largely draws attention in the areas of financial markets. This study also examines the characteristics of conditional volatility in the Baltic Stock Markets (Estonia, Latvia and Lithuania) by using a broad range of GARCH volatility models. Correctly forecasting the volatility leads to better understanding and managing financial market risk. Daily returns from four Baltic stock indexes are used; Estonia (TALSE index), Latvia (RIGSE index), Lithuania (VILSE index) and synthetic BALTIC benchmark index. We test a large family of GARCH models, including; the basic GARCH model, GARCH‐in‐mean model, asymmetric exponential GARCH and GJR GARCH, power GARCH and component GARCH model. We find strong evidence that daily returns from Baltic Stock Markets can be successfully modelled by GARCH‐type models. For all Baltic markets, we conclude that increased risk will not necessarily lead to a rise in the returns. All of the analysed indexes exhibit complex time series characteristics involving asymmetry, long tails and complex autoregression in the returns. Results from this study are firmly recommended to financial officers and international investors. Santrauka Straipsnyje analizuojamas salyginis Baltijos vertybiniu popieriu rinku (Estijos, Latvijos ir Lietuvos) nepastovumas, taikant eile GARCH kintamumo modeliu. Pažymetina, kad tinkamai prognozuojant nepastovuma, galima geriau suvokti ir valdyti finansiniu rinku rizika. Straipsnyje remiamasi keturiu Baltijos šaliu kasdienemis akciju indeksu gražomis; Estijos (TALSE indeksu), Latvijos (RIGSE indeksu), Lietuvos (VILSE indeksu) ir sintetiniu palyginamuoju BALTIC indeksu. Pritaikius eile GARCH kintamumo modeliu, galima teigti, kad didejanti rizika Baltijos šaliu rinkose nebūtinai itakos vertybiniu popieriu gražos augima. Tyrimo metu gauti rezultatai rekomenduojami finansu specialistams ir investuotojams.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

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