A characterization of vector autoregressive processes with common cyclical features

This paper presents necessary and sufficient conditions for the existence of common cyclical features in Vector Auto Regressive (VAR) processes integrated of order 0, 1, 2, where the common cyclical features correspond to common serial correlation (CS), commonality in the final equations (CE) and co-dependence (CD). The results are based on local rank factorizations of the reversed AR polynomial around the poles of its inverse. All processes with CS structures are found to present also CE structures and vice versa. The presence of CD structures, instead, implies the presence of both CS and CE structures, but not vice versa. Characterizations of the CS, CE, CD linear combinations are given in terms of linear subspaces defined in the local rank factorizations.     

The conditional autoregressive Wishart model for multivariate stock market volatility

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks.     

The adequacy of asymptotic approximations in the near-integrated autoregressive model with dependent errors

We consider the normalized least squares estimator of the parameter in a nearly integrated first-order autoregressive model with dependent errors. In a first step we consider its asymptotic distribution as well as asymptotic expansion up to order O_p(T⁻¹). We derive a limiting moment generating function which enables us to calculate various distributional quantities by numerical integration. A simulation study is performed to assess the adequacy of the asymptotic distribution when the errors are correlated. We focus our attention on two leading cases: MA(1) errors and AR(1) errors. The asymptotic approximations are shown to be inadequate as the MA root gets close to −1 and as the AR root approaches either −1 or 1. Our theoretical analysis helps to explain and understand the simulation results of Schwert (1989) and DeJong, Nankervis, Savin, and Whiteman (1992) concerning the size and power of Phillips and Perron's (1988) unit root test. A companion paper, Nabeya and Perron (1994), presents alternative asymptotic frameworks in the cases where the usual asymptotic distribution fails to provide an adequate approximation to the finite-sample distribution.     

The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach

This paper investigates the impact of credit supply shocks on the macroeconomy and estimates a new financial conditions index. We calculated two credit supply factors using a time-varying parameter FAVAR model. The first factor is identified as the willingness to lend, while the second factor is the lending capacity. The impact of these two types of shocks and their changes over time is examined using Hungarian data. The two types of lending shocks affect macro variables rather differently: a positive lending capacity shock (in a banking system mostly owned by non-residents) influences GDP through a decrease in country risk and the easing of monetary policy, while willingness to lend primarily increases lending activity. The two financial shocks also differ in terms of their evolution over time: deviations from the average in the impact of a willingness to lend shock usually occur for short periods of time and are of a small degree between the various quarters. However, in the case of lending capacity, certain trends can be observed: before the crisis, the stability of the banking system played an increasing role in country risk, whereas after 2008 it appears that monetary policy paid increasing attention to financial stability. Finally, a new type of financial conditions index is quantified based on our estimates, which measures the impact of the banking system’s lending activity on GDP growth.     

The dynamic relationship between stock,bond and foreign exchange markets

This paper investigates whether the deviation of a currency from its fundamentally determined rate of return affects the relationship between interest rates and stock market yields. A time-varying transition probability, the Markov-switching vector autoregressive (MS-VAR) model, is utilized for this purpose. Wald and likelihood ratio tests are computed and used as model adequacy measures. In order to analyze the link between the variables, impulse–response functions are employed. A sticky price exchange rate model is used to show the fundamentally determined rate of return of currencies. States are defined as either overvalued or undervalued, depending on the position of the observed exchange rate compared to its fundamentally determined rate. The model is applied to four major currencies: the Australian Dollar, the Canadian Dollar, the Japanese Yen, and the British Pound. Transition between the states is linked to the risk-adjusted excess return (the Sharpe ratio) of the debt and equity markets of the respective currencies in order to understand whether over- and undervaluation is connected to the returns in these markets. The results provide evidence that the relationship between economic fundamentals and nominal exchange rates are subject to change depending on the over- or undervaluation of the currencies relative to their fundamentally determined rate of return. An extension of this result shows that the Sharpe ratios of debt and equity investments in the currencies influence the evolution of the transitional dynamics of the exchange rates’ deviation from their fundamental values.