Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by inflation and interest rate expectations as well...

We comprehensively analyze the predictive power of several option implied variables for monthly S & P 500 excess returns and realized variance. The correlation risk premium (CRP) emerges as a strong predictor of both excess returns and realized variance. This is true both in- and out-of-sample....

This paper studies a basket of risk statistics that are widely used to measure investment performance. Those risk statistics were used to rank the performance of the assets. The dependent information was removed from the set of risk measures that were used in the test. The risk statistics were...

Stochastic optimisation has found a fertile ground for applications in finance. One of the greatest challenges remains to incorporate a set of scenarios that accurately models the behaviour of financial markets, and in particular their behaviour during crashes and crises, without sacrificing the...

Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to...

A great number of academic papers evaluate the potential for incentive-driven bias in sell-side analysts' earnings forecasts. Yet bias does not necessarily invalidate a forecast, nor does it impinge on its relative quality. We find that analysts' forecasts are optimistic relative to recently...

Our research on data for the S&P 500 ETF from 1993-2013 documents an intraday momentum pattern: the first half-hour return on the market (from the previous day's close) predicts the last half-hour return. The predictability, both statistically and economically significant, is stronger on more...

This paper defines systematic value investing as an empirical optimization problem. Predictive modeling is introduced as a systematic value investing methodology with dynamic and optimization features. A predictive modeling process is demonstrated using financial metrics from Gray & Carlisle and...