We consider the issue of Block Bootstrap methods in processes that exhibit strong dependence. The main difficulty is to transform the series in such way that implementation of these techniques can provide an accurate approximation to the true distribution of the test statistic under...

The forward premium anomaly refers to the situation where the slope coefficient in a regression of spot returns on the lagged interest rate differential is negative and significantly different to unity. This paper explores some of the asymmetries and non linearities present in the anomaly and...

This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing the intercept to follow a slowly varying...

Daily futures returns on six important commodities are found to be well described as FIGARCH fractionally integrated volatility processes, with small departures from the martingale in mean property. The paper also analyzes several years of high frequency intra day commodity futures returns and...

This paper explores a semiparametric version of a time-varying regression, where a subset of the regressors have a fixed coefficient and the rest a time-varying one. We provide an estimation method and establish associated theoretical properties of the estimates and standard errors in extended...

We estimate the fiscal (spending) multiplier using quarterly U.S. data, 1986-2017. We define government spending shocks as actual minus expected expenditure growth, the latter obtained from the Survey of Professional Forecasters. We employ the ST-VAR model with the local projections method. A...

We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially...

We study the real-time characteristics and drivers of jumps in option prices. To this end, we employ high frequency data from the 24-hour E-mini S&P 500 options market. We find that option prices do not jump simultaneously across strikes and maturities and are uncorrelated with jumps in the...

In this paper we introduce the general setting of a multivariate time series autoregressive model with stochastic time-varying coefficients and time-varying conditional variance of the error process. This allows modeling VAR dynamics for non-stationary times series and estimation of time varying...

Following Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time...