scholarly journals Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices

2021 ◽  
Author(s):  
Chiara Casoli ◽  
Riccardo (Jack) Lucchetti
Keyword(s):  
Factor Model ◽  
Commodity Prices ◽  
Standard Technique ◽  
Factor Models ◽  
Monte Carlo Experiment ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Common Component ◽  
Stable Pattern

Abstract We propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term non-stationary and a short-term stationary part. A Monte Carlo experiment shows that incorporating the cointegration structure into the DFM leads to a better reconstruction of the space spanned by the factors, compared to the most standard technique of applying a factor model in differenced systems. We apply our procedure to a set of commodity prices to analyse the comovement among different markets and find that commodity prices move together mostly due to long-term common forces; while the trend for the prices of most primary goods is declining, metals and energy exhibit an upward or at least stable pattern since the 2000s.

scholarly journals MODELING LONGEVITY RISK WITH GENERALIZED DYNAMIC FACTOR MODELS AND VINE-COPULAE

2015 ◽  
Vol 46 (1) ◽  
pp. 165-190 ◽  
Author(s):  
Helena Chuliá ◽  
Montserrat Guillén ◽  
Jorge M. Uribe
Keyword(s):  
Factor Model ◽  
Risk Measures ◽  
Mortality Rates ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mortality Risks ◽  
Static Factor ◽  
Vine Copula ◽  
The Uk

AbstractWe present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses generalized dynamic factor models fitted to the differences in the log-mortality rates. We compare their prediction performance with that of models previously described in the literature, including the traditional static factor model fitted to log-mortality rates. We also construct risk measures using vine-copula simulations, which take into account the dependence between the idiosyncratic components of the mortality rates. The methodology is applied to forecast mortality rates for a population portfolio for the UK and to estimate longevity and mortality risks.

scholarly journals Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models*

2018 ◽  
Vol 17 (3) ◽  
pp. 462-494 ◽  
Author(s):  
Matteo Barigozzi ◽  
Marc Hallin ◽  
Stefano Soccorsi
Keyword(s):  
Financial Markets ◽  
Sovereign Debt ◽  
Factor Model ◽  
Debt Crisis ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Heterogeneous Effects ◽  
Global And Local ◽  
General Dynamic

AbstractWe employ a two-stage general dynamic factor model to analyze co-movements between returns and between volatilities of stocks from the U.S., European, and Japanese financial markets. We find two common shocks driving the dynamics of volatilities—one global shock and one United States–European shock—and four local shocks driving returns, but no global one. Co-movements in returns and volatilities increased considerably in the period 2007–2012 associated with the Great Financial Crisis and the European Sovereign Debt Crisis. We interpret this finding as the sign of a surge, during crises, of interdependencies across markets, as opposed to contagion. Finally, we introduce a new method for structural analysis in general dynamic factor models which is applied to the identification of volatility shocks via natural timing assumptions. The global shock has homogeneous dynamic effects within each individual market but more heterogeneous effects across them, and is useful for predicting aggregate realized volatilities.

An empirical testing of comparative efficiency of static and dynamic factor models towards stock returns’ predictability in capital market of Pakistan

2020 ◽  
Vol 4 (2) ◽  
pp. 141-162
Author(s):  
Laila Taskeen Qazi ◽  
Atta ur Rahman ◽  
Shahid Ali ◽  
Sohail Alam
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Time Series Data ◽  
Factor Model ◽  
Efficient Market Hypothesis ◽  
Factor Models ◽  
Series Data ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Returns Predictability

Efficient Market Hypothesis has its supporters and critics as it has invited significant attention of research scholarship in recent years. The taxonomy and existence of this hypothesis is widely debated in terms of making economic decisions in the capital markets. Stock returns predictability has galvanized researchers to use forecasting models. Literature shows that forecasting is possible yet it debates problems associated with the techniques used for forecasting from the time series data. The study relies on stock returns for 67 randomly selected companies listed on the Pakistan Stock Exchange. The static and the dynamic factor models are compared in terms of forecast efficiency. The study also uses eight macroeconomic variables to forecast stock returns by including gold prices, crude oil prices, market capitalization, PSX- 100 index, PSX-100 index turnover, KIBOR 1-month rates, KIBOR 3 years rates and Rupee to Dollar rates. The results of the hit rates and out-of-sample forecasting technique suggest that dynamic factor model is the best multivariate time series forecasting model in the Pakistani context.

scholarly journals A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU

2021 ◽  
Author(s):  
Cosimo Magazzino ◽  
Marco Mele
Keyword(s):  
Neural Networks ◽  
Factor Model ◽  
Common Factor ◽  
Back Propagation ◽  
Dynamic Factor ◽  
Common Component ◽  
Economic Cycles ◽  
Composite Nature ◽  
Empirical Demonstration ◽  
Public Revenues

AbstractThis paper shows that the co-movement of public revenues in the European Monetary Union (EMU) is driven by an unobserved common factor. Our empirical analysis uses yearly data covering the period 1970–2014 for 12 selected EMU member countries. We have found that this common component has a significant impact on public revenues in the majority of the countries. We highlight this common pattern in a dynamic factor model (DFM). Since this factor is unobservable, it is difficult to agree on what it represents. We argue that the latent factor that emerges from the two different empirical approaches used might have a composite nature, being the result of both the more general convergence of the economic cycles of the countries in the area and the increasingly better tuned tax structure. However, the original aspect of our paper is the use of a back-propagation neural networks (BPNN)-DF model to test the results of the time-series. At the level of computer programming, the results obtained represent the first empirical demonstration of the latent factor’s presence.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

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