We show that structural changes in stochastic volatility models induce spurious persistence. Other than in GARCH-type models, implied persistence does not tend to unity with given size of the structural change and increasing sample size.

This paper shows that the excessive volatility results in spurious regressions. The spuriousness can be driven by persistency in the error variances unlike the conventional spurious regressions that are generated by the persistency in the level of regression errors.

Using a 219-year sample, we find that the US output growth and inflation volatilities fell by 60% and 76%, respectively, from 1945 until the mid-1960s. This Postwar Moderation is more substantial than the Great Moderation. The largest reduction in inflation volatility occurred during the...

This article provides a procedure for the estimation of parametric homogeneous stochastic volatility (SV) pricing formulae based on option data. Our estimator has the advantage of being (i) based on option data, (ii) easy to implement in practice, (iii) with clear statistic properties and (iv)...

This paper proposes a couple of new methods to compute the news impact curve for stochastic volatility (SV) models. The new methods incorporate the joint movement of return and volatility, which has been ignored by the extant literature. The first method employs the Bayesian Markov chain Monte...

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed...

We estimate the long rate and its volatility within the Svensson framework. The procedure that best extrapolates the longest observable rate and its volatility is a 2-dimensional grid search conditioned on the ridge regression suggested by Annaert et al. (2013).

We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.

This paper extends the classical work of bipower variation by allowing the return process to be autocorrelated. We propose a method of estimating the return volatility when the price process is described by a fractal Brownian motion with jumps.

This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The quantile coupling allows one to apply the standard Gaussian-based estimation and inference to the transformed data set. The resulting estimator is asymptotically normal with a...