We study implications of unpriced "granular measurement errors" -- idiosyncratic shocks to large firms that aren't well-diversified in market indices -- for asset pricing tests and propose alternative tests insensitive to them. We find stronger evidence of an intertemporal relation between the...

Backtesting risk measures represents a challenge and complex methods are often required. In this paper, we propose a new framework for backtesting that can be applied to every law invariant risk measures. We base our approach on the formalization of the concept of level of coverage associated...

The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...

The aim of the presented study was to assess the quality of VaR forecasts in various states of the economic situation. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were made on the daily closing prices of 10 major indices in...

We propose a heterogeneous autoregressive (HAR) model with time-varying parameters in the form of a local linear random forest. In contrast to conventional random forests that approximate the volatility nonparametrically using local averaging, the building blocks of our forest are HAR panel...

We propose a widely applicable bootstrap based test of the null hypothesis of equality of two firms' Risk Measures (RMs) at a single point in time. The test can be applied to any market-based measure. In an iterative procedure, we can identify a complete grouped ranking of the RMs, with...

The two basic questions that every investor tries to answer before investment are questions about predicting return and risk. Risk and return are generally considered two positively correlated sizes, during the growth of risk it is expected increase of return to compensate the higher risk. The...

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for...