Research

Abstract:

We revisit the problem of estimating high-dimensional global bank network connectedness. Instead of directly regularizing the high-dimensional vector of realized volatilities as in Demirer et al. (2018), we estimate a dynamic factor model with sparse VAR idiosyncratic components. This allows to disentangle: (I) the part of system-wide connectedness (SWC) due to the common component shocks (what we call the "banking market"), and (II) the part due to the idiosyncratic shocks (the single banks). We employ both the original dataset as in Demirer et al. (2018) (daily data, 2003-2013), as well as a more recent vintage (2014-2023). For both, we compute SWC due to (I), (II), (I+II) and provide bootstrap confidence bands. In accordance with the literature, we find SWC to spike during global crises. However, our method minimizes the risk of SWC underestimation in high-dimensional datasets where episodes of systemic risk can be both pervasive and idiosyncratic. In fact, we are able to disentangle how in normal times ≈60-80% of SWC is due to idiosyncratic variation and only ≈20-40% to market variation. However, in crises periods such as the 2008 financial crisis and the Covid19 outbreak in 2019, the situation is completely reversed: SWC is comparatively more driven by a market dynamic and less by an idiosyncratic one. 

Abstract:

We reconcile the two worlds of dense and sparse modeling by exploiting the positive aspects of both. We employ a dynamic factor model and assume the idiosyncratic term follows a sparse vector autoregressive model (VAR) which allows for cross-sectional and time dependence. The estimation is articulated in two steps: first, the factors and their loadings are estimated via principal component analysis and second, the sparse VAR is estimated by regularized regression on the estimated idiosyncratic components. We prove consistency of the proposed estimation approach as the time and cross-sectional dimension diverge. In the second step, the estimation error of the first step needs to be accounted for. Here, we do not follow the naive approach of simply plugging in the standard rates derived for the factor estimation. Instead, we derive a more refined expression of the error. This enables us to derive tighter rates. We discuss the implications to forecasting and semi-parametric estimation of the inverse of the spectral density matrix and we complement our procedure with a joint information criteria for the VAR lag-length and the number of factors. The finite sample performance is illustrated by means of an extensive simulation exercise. Empirically, we assess the performance of the proposed method for macroeconomic forecasting using the FRED-MD dataset.

Abstract:

In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowledge of the order of integration of the time series under consideration. We augment the VAR with at least as many lags as the suspected maximum order of integration, an approach which has been proven to be robust against the presence of unit roots in low dimensions. We prove that we can restrict the augmentation to only the variables of interest for the testing, thereby making the approach suitable for high dimensions. We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables. We then establish uniform asymptotic normality of a second-stage regression involving only the selected variables. Finite sample simulations show good performance, an application to investigate the (predictive) causes and effects of economic uncertainty illustrates the need to allow for unknown orders of integration.

Abstract:

In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global temperatures. By allowing for high dimensionality in the model we can enrich the infor- mation set with all relevant natural and anthropogenic forcing variables to obtain reliable causal relations. These variables have mostly been investigated in an aggregated form or in separate models in the previous literature. Additionally, our framework allows to ignore the order of inte- gration of the variables and to directly estimate the VAR in levels, thus avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are well known to contain stochastic trends as well as yielding long memory. We are thus able to display the causal networks linking radiative forcings to global temperatures but also to causally connect radiative forcings among themselves, therefore allowing for a careful reconstruction of a timeline of causal effects among forcings. The robustness of our proposed procedure makes it an important tool for policy evaluation in tackling global climate change.

Abstract:

The least squares estimator can be cast to depend only on the precision matrix. We show that a consistent estimator of the latter can be directly used to obtain a consistent estimator of the former even in high-dimensional regression problems where the number of covariates can be larger than the sample size. We call this the precision least squares estimator. We show that it is asymptotically Gaussian and delivers uniformly valid inference irrespective of the sparsity within the data generating process. Since bias can still hinder the estimates when using consistent but regularized precision matrix estimators, we show how to construct a nearly unbiased least squares estimator. We illustrate the relevance of regularized precision matrix on both simulated and real data. Contrary to the systemic risk literature based on multivariate autoregressive models for stock returns, and more in line with the theory of financial market fragility, we find evidence that returns connectedness of 88 global banks drastically decreases during crisis periods.

Abstract:

This paper is concerned with the estimation of aggregate relationships among a potentially very large number of panel data variables in the presence of unobserved heterogeneity in the form of interactive effects, an empirically very relevant scenario that has not been considered before. One of our findings is that if the regressors load on the same set of latent factors as the dependent variable, which seems a priori likely since many variables are co-moving, the aggregation automatically accounts for the unobserved heterogeneity. In order to also account for the many regressors, the aggregate model is estimated using LASSO, leading to the ``Cross-sectionally Averaged aDAptive LASSO'' (CADA-LASSO). It is shown that under suitable regulatory conditions, the new estimator is oracle efficient and selection consistent, properties that are verified in small samples using Monte Carlo simulations. The empirical usefulness of the estimator is illustrated using as an example the gravity equation of trade.