scholarly journals Using the Entire Yield Curve in Forecasting Output and Inflation

Econometrics ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 40
Author(s):  
Eric Hillebrand ◽  
Huiyu Huang ◽  
Tae-Hwy Lee ◽  
Canlin Li
Keyword(s):  
Monte Carlo Simulation ◽  
Yield Curve ◽  
Factor Model ◽  
Factor Models ◽  
Output Growth ◽  
Numerical Examples ◽  
Combining Forecasts ◽  
Out Of Sample ◽  
The Many ◽  
Combining Information

In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC factors. These PC or NS factors are combining information (CI) in the predictors (yields). However, these CI factors are not “supervised” for a specific forecast target in that they are constructed by using only the predictors but not using a particular forecast target. In order to “supervise” factors for a forecast target, we follow Chan et al. (1999) and Stock and Watson (2004) to compute PC or NS factors of many forecasts (not of the predictors), with each of the many forecasts being computed using one predictor at a time. These PC or NS factors of forecasts are combining forecasts (CF). The CF factors are supervised for a specific forecast target. We demonstrate the advantage of the supervised CF factor models over the unsupervised CI factor models via simple numerical examples and Monte Carlo simulation. In out-of-sample forecasting of monthly US output growth and inflation, it is found that the CF factor models outperform the CI factor models especially at longer forecast horizons.

scholarly journals Um Modelo de Fatores Latentes com Variáveis Macroeconômicas para a Curva de Cupom Cambial

2007 ◽  
Vol 5 (1) ◽  
pp. 79 ◽  
Author(s):  
Felipe Pinheiro ◽  
Caio Ibsen Rodrigues de Almeida ◽  
José Valentim Vicente
Keyword(s):  
Random Walk ◽  
Term Structure ◽  
Legendre Polynomials ◽  
Yield Curve ◽  
Factor Model ◽  
Impulse Response Function ◽  
Factor Models ◽  
Latent Factors ◽  
Macroeconomic Variables ◽  
Out Of Sample

Recently, a myriad of factor models including macroeconomic variables have been proposed to analyze the yield curve. We present an alternative factor model where term structure movements are captured by Legendre polynomials mimicking the statistical factor movements identified by Litterman e Scheinkmam (1991). We estimate the model with Brazilian Foreign Exchange Coupon data, adopting a Kalman filter, under two versions: the first uses only latent factors and the second includes macroeconomic variables. We study its ability to predict out-of-sample term structure movements, when compared to a random walk. We also discuss results on the impulse response function of macroeconomic variables.

Forecasting the yield curve of government bonds: a dynamic factor approach

2017 ◽  
Vol 43 (7) ◽  
pp. 774-793
Author(s):  
Walid Ben Omrane ◽  
Chao He ◽  
Zhongzhi Lawrence He ◽  
Samir Trabelsi
Keyword(s):  
Kalman Filter ◽  
Yield Curve ◽  
Factor Model ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Dynamic Approach ◽  
Yield Curves ◽  
Content Type ◽  
Forecasting Performance ◽  
Out Of Sample

Purpose Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics. Design/methodology/approach The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data. Findings The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance. Research limitations/implications The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency. Practical implications Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements. Social implications The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies. Originality/value The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.

Control of processing at multi-tool two-support setup

2020 ◽  
pp. 67-73
Author(s):  
N.D. YUsubov ◽  
G.M. Abbasova
Keyword(s):  
Degrees Of Freedom ◽  
Factor Model ◽  
Angular Displacement ◽  
Factor Models ◽  
Technological System ◽  
Displacement Control ◽  
Model Accuracy ◽  
Six Degrees Of Freedom ◽  
Angular Displacements ◽  
And Control

The accuracy of two-tool machining on automatic lathes is analyzed. Full-factor models of distortions and scattering fields of the performed dimensions, taking into account the flexibility of the technological system on six degrees of freedom, i. e. angular displacements in the technological system, were used in the research. Possibilities of design and control of two-tool adjustment are considered. Keywords turning processing, cutting mode, two-tool setup, full-factor model, accuracy, angular displacement, control, calculation [email protected]

An Affine Macro-Factor Model of the UK Yield Curve

2007 ◽  
Author(s):  
Peter M. Lildholdt ◽  
Nikolaos Panigirtzoglou ◽  
Chris Peacock
Keyword(s):  
Yield Curve ◽  
Factor Model ◽  
The Uk

scholarly journals Choosing Factors for the Vietnamese Stock Market

2021 ◽  
Vol 14 (3) ◽  
pp. 96
Author(s):  
Nina Ryan ◽  
Xinfeng Ruan ◽  
Jin E. Zhang ◽  
Jing A. Zhang
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Five Factor Model ◽  
Explanatory Power ◽  
Factor Models ◽  
Marginal Contribution ◽  
Return On Equity ◽  
Factor Modeling ◽  
Three Factor ◽  
Better Than

In this paper, we test the applicability of different Fama–French (FF) factor models in Vietnam, we investigate the value factor redundancy and examine the choice of the profitability factor. Our empirical evidence shows that the FF five-factor model has more explanatory power than the FF three-factor model. The value factor remains important after the inclusion of profitability and investment factors. Operating profitability performs better than cash and return-on-equity (ROE) profitability as a proxy for the profitability factor in FF factor modeling. The value factor and operating profitability have the biggest marginal contribution to a maximum squared Sharpe ratio for the five-factor model factors, highlighting the value factor (HML) non-redundancy in describing stock returns in Vietnam.

On portfolio optimization

2016 ◽  
Vol 17 (3) ◽  
pp. 295-309 ◽  
Author(s):  
Theo Berger ◽  
Christian Fieberg
Keyword(s):  
Minimum Variance ◽  
Factor Models ◽  
Scale Factor ◽  
Covariance Matrices ◽  
Portfolio Performance ◽  
Content Type ◽  
Portfolio Decisions ◽  
Multi Scale ◽  
Out Of Sample ◽  
Factor Returns

Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance. Findings The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account. Practical implications The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. Originality/value The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

Improving Mean Variance Optimization through Sparse Hedging Restrictions

2015 ◽  
Vol 50 (6) ◽  
pp. 1415-1441 ◽  
Author(s):  
Shingo Goto ◽  
Yan Xu
Keyword(s):  
Covariance Matrix ◽  
Factor Model ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Finite Samples ◽  
Inverse Covariance Matrix ◽  
Out Of Sample ◽  
Nonnegativity Constraints ◽  
Mean Variance ◽  
Equal Weighting

AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

scholarly journals Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models

2018 ◽  
Vol 6 (3) ◽  
pp. 68
Author(s):  
Hokuto Ishii
Keyword(s):  
Exchange Rate ◽  
Prediction Accuracy ◽  
Factor Model ◽  
Five Factor Model ◽  
Model Fitting ◽  
Extended Model ◽  
Out Of Sample ◽  
Exchange Rate Changes ◽  
Relative Spread ◽  
Future Exchange

This paper investigates the predictability of exchange rate changes by extracting the factors from the three-, four-, and five-factor model of the relative Nelson–Siegel class. Our empirical analysis shows that the relative spread factors are important for predicting future exchange rate changes, and our extended model improves the model fitting statistically. The regression model based on the three-factor relative Nelson–Siegel model is the superior model of the extended models for three-month-ahead out-of-sample predictions, and the prediction accuracy is statistically significant from the perspective of the Clark and West statistic. For 6- and 12-month-ahead predictions, although the five-factor model is superior to the other models, the prediction accuracy is not statistically significant.

scholarly journals Dynamic factor models: Does the specification matter?

SERIEs ◽  
2021 ◽  
Author(s):  
Karen Miranda ◽  
Pilar Poncela ◽  
Esther Ruiz
Keyword(s):  
Factor Models ◽  
Model Specification ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Number Of Factors ◽  
Time Varying Parameters ◽  
Out Of Sample ◽  
Mixed Frequency Data ◽  
Natural Way ◽  
Factor Estimation

AbstractDynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

INTERNATIONAL OUTPUT AND INFLATION UNCERTAINTY AND THEIR IMPACT ON COUNTRIES' MACROECONOMIC PERFORMANCE. EVIDENCE FROM A DYNAMIC FACTOR GARCH-IN-MEAN MODEL

2018 ◽  
Vol 22 (5) ◽  
pp. 1113-1133 ◽  
Author(s):  
Tino Berger ◽  
Sibylle Grabert
Keyword(s):  
Factor Model ◽  
Output Growth ◽  
Dynamic Factor ◽  
International Level ◽  
Macroeconomic Performance ◽  
Inflation Uncertainty ◽  
Inflation Rates ◽  
Oil Crisis ◽  
The Impact ◽  
Country Specific

We identify international output and inflation uncertainty and analyze their impact on individual countries' macroeconomic performance. Output and inflation uncertainty on an international level is measured through the conditional variances of common factors in inflation and output growth, estimated from a bivariate dynamic factor model with GARCH errors. The impact of international and country-specific uncertainty is analyzed by including the conditional variances as regressors. We find increases in uncertainty during the first and second oil crisis, the 1980s and 1990s recessions as well as the recent Great Recession to be confined to the international level. The effect of international uncertainty results to be highly significant and unambiguously negative on countries' output growth and inflation rates whereas the impact of country-specific uncertainty is very mixed.

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