We examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involves a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors.
We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters.
We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We study long historical data series of aggregate stock market volatility, starting in the 19th century, as in Schwert (1989).
Recent studies emphasize that survey-based inflation risk measures are informative about future inflation and thus useful for monetary authorities. However, these data are typically available at a quarterly frequency whereas monetary policy decisions require a more frequent monitoring of such risks.
Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis combinations.
We study a new class of conditional skewness models based on conditional quantiles regressions. The approach is much inspired by work of Hal White. To handle multiple horizons I consider quantile MIDAS regressions which amount to direct forecasting—as opposed to iterated forecasting—conditional skewness.
This paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York.
This paper deals with the estimation of the risk-return trade-off. We use a MIDAS model for the conditional variance and allow for possible switches in the risk-return relation through a Markov-switching specification. We find strong evidence for regime changes in the risk-return relation.
Volatility component models have received considerable attention recently, not only because of their ability to capture complex dynamics via a parsimonious parameter structure, but also because it is believed that they can handle well structural breaks or nonstationarities in asset price volatility.
We propose a general GARCH framework that allows one to predict volatility using returns sampled at a higher frequency than the prediction horizon.
We provide empirical evidence for the existence, magnitude, and economic cost of stigma associated with banks borrowing from the Federal Reserve's Discount Window (DW) during the 2007-2008 financial crisis.
We examine the effects of mixed sampling frequencies and temporal aggregation on the size of commonly used tests for cointegration, and we find that these effects may be severe.
We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach.
Many time series are sampled at different frequencies. When we study co-movements between such series we usually analyze the joint process sampled at a common low frequency.
When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002).
We propose a quantile-based measure of conditional skewness, particularly suitable for handling recalcitrant emerging market (EM) returns. The skewness of international stock market returns varies significantly across countries over time, and persists at long horizons.
This article presents a reduced-form model that contains frailty factors to predict mortgage default and develops a novel framework to model systematic risk of mortgages. We match default rates along multiple dimensions by extending the generalized autoregressive score (GAS) models.
Policy impact studies often suffer from endogeneity problems. Consider the case of the ECB Securities Markets Programme: If Eurosystem interventions were triggered by sudden and strong price deteriorations, looking at daily price changes may bias downwards the correlation between yields and the amounts of bonds purchased.
During retailer-initiated price wars (PWs), hundreds of brands are involved simultaneously, affecting brands’ and retailers’ positioning and ultimately making the performance outcome for individual brands difficult to predict. Likewise, the impact on brand performance after the PW, when prices are restored, is unclear.
Private labels or store brands have witnessed considerable growth in the last few decades, especially in grocery products. However, market shares of store brand vary considerably across categories, markets, and countries. A natural question of interest to academics and practitioners is what factors influence store brand market shares.