scholarly journals Principal component regression in GAMLSS applied to Greek–German government bond yield spreads

2021 ◽  
pp. 1471082X2110229
Author(s):  
D. Stasinopoulos Mikis ◽  
A. Rigby Robert ◽  
Georgikopoulos Nikolaos ◽  
De Bastiani Fernanda
Keyword(s):  
Additive Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Complex Nature ◽  
Government Bond ◽  
German Government ◽  
Yield Spreads ◽  
Explanatory Variables ◽  
Interaction Terms ◽  
Bond Yield

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.

scholarly journals Sparse principal component regression via singular value decomposition approach

2021 ◽  
Author(s):  
Shuichi Kawano
Keyword(s):  
Singular Value Decomposition ◽  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Singular Value ◽  
Decomposition Approach ◽  
Response Variable ◽  
Explanatory Variables ◽  
Value Decomposition

AbstractPrincipal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.

Importance of Fiscal Fundamentals for Sovereign Risk Spread

2018 ◽  
pp. 127-146
Author(s):  
Irena Szarowská
Keyword(s):  
Generalized Method Of Moments ◽  
Yield Spread ◽  
Government Bond ◽  
Sovereign Risk ◽  
Fiscal Discipline ◽  
Yield Spreads ◽  
Risk Premiums ◽  
Crisis Period ◽  
Bond Yield ◽  
Bond Risk

The chapter examines the importance of fiscal fundamentals for sovereign risk spread in the period of 1995-2015, and its goal is to test whether stronger fiscal discipline reduces sovereign risk premiums. The empirical evidence is based on unbalanced annual panel data of 15 EU countries (its time span is divided into a pre-crisis and a post-crisis period). The study applies the generalized method of moments. Evidence shows that before the financial crisis, investors generally ignored bond risk factors in individual countries, but that the spreads sharply diverged starting from the year 2008. The results confirm a statistically significant impact of fiscal fundamentals on government bond yield spread. The improvement of the governments' fiscal position reduces sovereign yield spread. In a post-crisis period, findings report the raising of the importance of fiscal variables for spread, and GDP growth became a major determinant of government bond yield spreads, followed by the budget balance and debt development.

scholarly journals 57 Channels (and Nothin on) - Does TV-News on the Eurozone Affect Government Bond Yield Spreads?

2018 ◽  
Author(s):  
Julia Wolfinger ◽  
Lars P. Feld ◽  
Ekkehard A. Koehler ◽  
Tobias Thomas
Keyword(s):  
Government Bond ◽  
Yield Spreads ◽  
Bond Yield ◽  
Tv News

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

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