In this paper, we review the most common specifications of discrete-time stochasticvolatility (SV) models and illustrate the major principles of corresponding MarkovChain Monte Carlo (MCMC) based statistical inference. We provide a hands-on approachwhich is easily implemented in empirical...

While stochastic volatility models improve on the option pricing error when compared to theBlack-Scholes-Merton model, mispricings remain. This paper uses mixed normalheteroskedasticity models to price options. Our model allows for significant negative skewnessand time varying higher order...

Driven by the rise in computational power, it has become popular to measure integrated variance with high-frequency squared returns. Though the squared return is a natural choice as a variance estimate, it is not the most efficient one for a given interval length. Extreme-value based estima-...

This paper extends the cross sectionally augmented panel unit root test proposed byPesaran (2007) to the case of a multifactor error structure. The basic idea is to exploitinformation regarding the unobserved factors that are shared by other time series in additionto the variable under...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-runconcept the specific dynamic driving the process is largely build upon a priori economicbelief rather than a thorough statistical modeling procedure. The two prevailing timeseries models, i.e. the exponential smooth...

Controllability of longer-term interest rates requires that the persis-tence of their deviations from the central bank's policy rate (i.e. thepolicy spreads) remains suffciently low. This paper applies fractionalintegration techniques to assess the persistence of policy spreads ofeuro area money...

The long memory characteristic of financial market volatility is well documentedand has important implications for volatility forecasting and optionpricing. When fitted to the same data, different volatility models calculate theunconditional variance differently and could have very different...

We propose a technique to avoid spurious detections of jumps in highfrequencydata via an explicit thresholding on available test statistics. Weprove that it eliminates asymptotically all spurious detections. MonteCarlo results show that it performs also well in finite samples. In DowJones...