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We design adaptive realized kernels to estimate the integrated volatility in a framework that combines a stochastic volatility model with leverage effect for the efficient price and a semiparametric microstructure noise model specified at the highest frequency. Some time dependence parameters of...
The Approximate Bayesian Computation (ABC) filter extends the particle filtering methodology to general state-space models in which the density of the observation conditional on the state is intractable. We provide an exact upper bound for the mean squared error of the ABC filter, and derive...
We develop a novel structural credit risk model that extends the original Merton model by allowing for stochastic interest rates and stochastic volatility. The model is estimated using Bayesian methods implemented via a Markov chain Monte Carlo algorithm, in light of the demonstrable advantages...
Standard risk metrics tend to underestimate the true risks of hedge funds because of serial correlation in the reported returns. Getmansky, Lo, and Makarov (2004) derive mean, variance, Sharpe ratio, and beta formulae adjusted for serial correlation. Following their lead, we derive adjusted...
This article is concerned with robust conditional variance and value-at-risk (VaR) estimation. Losses due to idiosyncratic events can have a disproportionate impact on traditional VaR estimates, upwardly biasing these estimates, increasing capital requirements, and unnecessarily reducing the...
Intraday return volatility is characterized by the contemporaneous presence of periodicity and long memory. This article proposes two new parameterizations of the intraday volatility process that account for both features: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional...
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