This work develops change-point methods for statistics of high-frequency data. The main interest is the volatility of an Itˆo semi-martingale, which is discretely observed over a fixed time horizon. We construct a minimax-optimal test to discriminate different smoothness classes of the...

Information flows across international financial markets typically occur within hours, making volatility spillover appear contemporaneous in daily data. Such simultaneous transmission of variances is featured by the stochastic volatility model developed in this paper, in contrast to usually...

We present a general framework for testing the accuracy of Value-at-Risk (VaR) forecasts. The approach is based on the observation that violations – the days on which portfolio losses exceed the VaR – should be unpredictable. Specifically, these violations form a martingale difference...

Techniques for simulated maximum likelihood (SML) estimation, filtering, and assessing the fit of stochastic volatility models are examined. Both one- and two-factor models (with leverage effects) are considered. The techniques are computationally efficient, robust, straightforward to implement,...

Basel II Pillar 3 reports provide information about banks' exposure towards a number of risk factors, such as corporate credit risk and interest rate risk. Previous studies nd that the quality of such information is likely to be weak. We analyze the marginal contribution of pillar 3 exposure...

We analyze the theoretical moments of a nonlinear approximation to a model of business cycles and asset pricing with stochastic volatility and recursive preferences. We find that heteroskedastic volatility operationalizes a time-varying risk adjustment channel that induces variability in...

In this paper we provide an asymptotic distribution theory for some non-parametric tests of the hypothesis that asset prices have continuous sample paths. We study the behaviour of the tests using simulated data and see that certain versions of the tests have good finite sample behaviour. We...

Kim, Shephard and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a very fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method ruled out the leverage effect, which limited its scope for applications. Despite this, their basic method has been...

Importance sampling is used in many aspects of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this...

This paper provides limit distribution results for power variation, that is sums of powers of absolute increments, for certain types of time-changed Brownian motion and $\alpha $-stable processes. Special cases of these processes are stochastic volatility models used extensively in financial...