We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....

This paper identifies supply and demand shocks that are specific to the oil-market, and separates them from economy-wide shocks that affect the demand for many asset classes, including oil. The shocks are identified by the sign and magnitude of the correlation between daily oil price percent...

When aggregate stock market returns are directly regressed on the dividend-price ratio, the statistical evidence supporting the hypothesis that returns are predictable is mixed. One response in the literature has been to employ indirect return predictability tests, which use an approximate...

In this paper we investigate the forecast performance of nonlinear error-correction models with regime switching. In particular, we focus on threshold and Markov-switching error-correction models where adjustment towards long-run equilibrium is nonlinear and discontinuous. Our simulation study...

The volatility of equity and foreign exchange market is an important input to portfolio selection and to asset pricing models. Many investment decisions and valuation of derivatives frequently rely on predictions of volatility. In this paper we review the existing empirical literature in...

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...

In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on...

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a...

A couple of recent papers have shifted the focus towards disagreement of professional forecasters. When dealing with survey data that is sampled at a frequency higher than annual and that includes only fixed event forecasts, e.g. expectation of average annual growth rates measures of...