We use Deep Neural Networks to reconstruct GARCH parameters for empirical financial time series. The algorithm we develop, allows us to fit autocovariance of squared returns of financial data, with certain time lags, the second order statistical moment and the fourth order standardised moment....

In this short paper we try to systematically address and answer some question arising in the daily practice of hedge fund risk management. Nowadays several intraday measures of risk are available to the risk manager, but the informativeness of these higher frequency statistics is not yet...

We measure bond and stock conditional return volatility as a function of changes in sentiment, proxied by six indicators from the Tel Aviv Stock Exchange. We find that changes in sentiment affect conditional volatilities at different magnitudes and often in an opposite manner in the two markets,...

In this paper, we develop an aggregation procedure using time-varying weights for constructing the common component in international economic fluctuations. The methodology for deriving time-varying weights is based on some stylized features of the data documented in the paper. The model allows...