We follow the seminal work of Paelinck (1978) who introduces spatial interdependence of, i.e. income, expenditure, investment, to classic Keynesian economic models, and estimate a spatial factor model. Asset prices may display characteristics of spatial dependence meaning spatial proximity can...

We analyze a recently proposed spatial autoregressive model for stock returns and compare it to a one-factor model and the sample covariance matrix. The influence of refinements to these covariance estimation methods is studied. We employ power mapping as a noise reduction technique for the...

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IVS). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is...

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned...

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to...