Showing results 1 to 10 of approximately 10.(refine search)
Constructing Zero-Beta VIX Portfolios with Dynamic CAPM
This paper focuses on actively managed portfolios of VIX derivatives constructed to reduce portfolio correlation with the equity market. We find that the best results are obtained using Kalman filter-based dynamic CAPM. The portfolio construction method is capable of constructing zero-beta portfolios with positive alpha.
Dynamic Methods for Analyzing Hedge-Fund Performance: A Note Using Texas Energy-Related Funds
We apply dynamic regression to Texas energy-related hedge funds to track changes in portfolio structure and manager performance in response to changing oil prices. We apply hidden Markov models to compute shifts in portfolio performance from boom to bust states. Using these dynamic methods, we find that, in the recent oil-price decline, these funds raised their exposure to high-grade energy-related bonds in a bet that the spread to low-grade energy bonds would widen. When the high-grade bonds eventually fell, the hedge funds entered into a bust state.
Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications
We show that a model where investors learn about the persistence of oil-price movements accounts well for the fluctuations in oil-price futures since the late 1990s. Using a DSGE model, we then show that this learning process alters the impact of oil shocks, making it time-dependent and consistent with the muted impact oil-price changes had on macroeconomic outcomes during the early 2000s and again over the past two years. The Spring 2008 increase in oil prices had a larger impact because market participants considered that it was likely driven by permanent shocks.
Estimating Dynamic Macroeconomic Models : How Informative Are the Data?
Central banks have long used dynamic stochastic general equilibrium (DSGE) models, which are typically estimated using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off-the-shelf DSGE model applied to quarterly Euro Area data from 1970:3 to 2009:4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian ...
Macroeconomic nowcasting and forecasting with big data
Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions?the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before ?big data? became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other ...
Inferring the Shadow Rate from Real Activity
We estimate a shadow rate consistent with the paths of time series capturing real activity. This allows us to quantify the real effects of unconventional monetary policy in terms of equivalent short-term interest rate movements. We find that large-scale asset purchases and forward guidance had significant real effects equivalent of up to a four percent reduction in the federal funds rate.
Measuring the Natural Rate of Interest : A Note on Transitory Shocks
We present evidence that the natural rate of interest is buffeted by both permanent and transitory shocks. We establish this result by estimating a benchmark model with Bayesian methods and loose priors on the unobserved drivers of the natural rate. When subject to transitory shocks, the median estimate for the U.S. economy is more procyclical, displays a less marked secular decline, and is therefore higher following the Great Recession than most estimates in the literature.
Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson ...
Measuring the Natural Rate of Interest : International Trends and Determinants
U.S. estimates of the natural rate of interest ? the real short-term interest rate that would prevail absent transitory disturbances ? have declined dramatically since the start of the global financial crisis. For example, estimates using the Laubach-Williams (2003) model indicate the natural rate in the United States fell to close to zero during the crisis and has remained there through the end of 2015. Explanations for this decline include shifts in demographics, a slowdown in trend productivity growth, and global factors affecting real interest rates. This paper applies the ...
Indeterminacy and Imperfect Information
We study equilibrium determination in an environment where two kinds of agents have different information sets: The fully informed agents know the structure of the model and observe histories of all exogenous and endogenous variables. The less informed agents observe only a strict subset of the full information set. All types of agents form expectations rationally, but agents with limited information need to solve a dynamic signal extraction problem to gather information about the variables they do not observe. We show that for parameter values that imply a unique equilibrium under full ...