Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

This paper proposes a nonparametric test of causality in quantile. Zheng (1998) has proposed an idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng’s approach to the case of dependent...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm...

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is...

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...

For more than fifty years, the Solow decomposition (Solow 1957) has served as the standard measurement of total factor productivity (TFP) growth in economics and management, yet little is known about its precision, especially when the capital stock is poorly measured. Using synthetic data...

This paper aims to study the quantitative significance of lumpy labor adjustment as a propagation mechanism for business cycles. In the baseline model, I introduce lumpy job turnover in the spirit of Taylor (1980) and Calvo (1983) in a DSGE framework and find that it performs as same as the...

This paper discusses the paper "The Source of Historical Economic Fluctuations: An Analysis using Long-Run Restrictions" by Neville Francis and Valerie A. Ramey. It argues that these authors have made great progress both in the precise measurement of labor input as well as determining the effect...

The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a posterior distribution spanning potentially nonnested models with parameter spaces of different dimensionality. We use the method to jointly sample from an ARMA process of unknown...