Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)

We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these "dynamic dependence network" models shows how the individual series can be "decoupled" for sequential analysis, and then "recoupled" for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked-- for later recoupling-- through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models, and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currency, stock indices and commodity prices, the case study includes evaluations of multi-day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach

This paper surveys existing factor forecast applications for real economic activity and inflation by means of a meta-analysis and contributes to the current debate on the determinants of the forecast performance of large-scale dynamic factor models relative to other models. We find that, on average, factor forecasts are slightly better than other models' forecasts. In particular, factor models tend to outperform small-scale models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. Our results further suggest that factor forecasts are better for US than for UK macroeconomic variables, and that they are better for US than for euro-area output; however, there are no significant differences between the relative factor forecast performance for US and euro-area inflation. There is also some evidence that factor models are better suited to predict output at shorter forecast horizons than at longer horizons. These findings all relate to the forecasting environment (which cannot be influenced by the forecasters). Among the variables capturing the forecasting design (which can, by contrast, be influenced by the forecasters), the size of the dataset from which factors are extracted seems to positively affect the relative factor forecast performance. There is some evidence that quarterly data lend themselves better to factor forecasts than monthly data. Rolling forecasts are preferable to recursive forecasts. The factor estimation technique seems to matter as well. Other potential determinants - namely whether forecasters rely on a balanced or an unbalanced panel, whether restrictions implied by the factor structure are imposed in the forecasting equation or not and whether an iterated or a direct multi-step forecast is made - are found to be rather irrelevant. Moreover, we find no evidence that pre-selecting the variables to be included in the panel from which factors are extracted helped to improve factor forecasts in the past. --Factor models,forecasting,meta-analysis

Estimating multi-country VAR models

This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian: a prior flexibly reduces the dimensionality of the model and puts structure on the time variations; MCMC methods are used to obtain posterior distributions; and marginal likelihoods to check the fit of various specifications. Impulse responses and conditional forecasts are obtained with the output of MCMC routine. The transmission of certain shocks across G7 countries is analyzed. JEL Classification: C3, C5, E5Flexible priors, International transmission, Markov Chain Monte Carlo methods, Multi country VAR

Taking the temperature – forecasting GDP growth for mainland China

We present a new composite leading indicator of economic activity in mainland China, es-timated using a dynamic factor model. Our leading indicator is constructed from three se-ries: exports, a real estate climate index, and the Shanghai Stock Exchange index. These series are found to share a common, unobservable element from which our indicator can be identified. This indicator is then incorporated into out-of-sample one-step-ahead forecasts of Chinese GDP growth. Recursive out-of-sample accuracy tests indicate that the small-scale factor model approach leads to a successful representation of the sample data and provides an appropriate tool for forecasting Chinese business conditions.forecasting; China; leading indicator; factor model; growth cycles

Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation

This paper evaluates the performance of static and dynamic factor models for forecasting Canadian real output growth and core inflation on a quarterly basis. We extract the common component from a large number of macroeconomic indicators, and use the estimates to compute out-of-sample forecasts under a recursive and a rolling scheme with different window sizes. Forecasts from factor models are compared with those from AR(p) models as well as IS- and Phillips-curve models. We find that factor models can improve the forecast accuracy relative to standard benchmark models, for horizons of up to 8 quarters. Forecasts from our proposed factor models are also less prone to committing large errors, in particular when the horizon increases. We further show that the choice of the sampling-scheme has a large influence on the overall forecast accuracy, with smallest rolling-window samples generating superior results to larger samples, implying that using "limited-memory" estimators contribute to improve the quality of the forecasts.Econometric and statistical methods

Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package

Raftery, Karny, and Ettler (2010) introduce an estimation technique, which they refer to as Dynamic Model Averaging (DMA). In their application, DMA is used to predict the output strip thickness for a cold rolling mill, where the output is measured with a time delay. Recently, DMA has also shown to be useful in macroeconomic and financial applications. In this paper, we present the eDMA package for DMA estimation implemented in R. The eDMA package is especially suited for practitioners in economics and finance, where typically a large number of predictors are available. Our implementation is up to 133 times faster then a standard implementation using a single-core CPU. Thus, with the help of this package, practitioners are able to perform DMA on a standard PC without resorting to large clusters, which are not easily available to all researchers. We demonstrate the usefulness of this package through simulation experiments and an empirical application using quarterly U.S. inflation data.Comment: 21 pages, 5 figures, 2 table

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory

Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore

We apply multivariate statistical methods to a large dataset of Singapore’s macroeconomic variables and global economic indicators with the objective of forecasting business cycles in a small open economy. The empirical results suggest that three common factors are present in the time series at the quarterly frequency, which can be interpreted as world, regional and domestic economic cycles. This leads us to estimate a factor-augmented vector autoregressive (FAVAR) model for the purpose of optimally forecasting real economic activity in Singapore. By taking explicit account of the common factor dynamics, we find that iterative forecasts generated by this model are significantly more accurate than direct multi-step predictions based on the identified factors as well as forecasts from univariate and vector autoregressions.business cycles; principal components; dynamic factor model; factor-augmented VAR; forecasting; Singapore