The forecasting literature has identi ed two important issues: (i) several predictors have substantial and statistically signi cant predictive content, although only sporadically, and it is unclear whether this predictive content can be exploited reliably; (ii) in-sample predictive content does...

We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the...

We construct a framework for measuring economic activity in real time (e.g., minute-by-minute), using a variety of stock and flow data observed at mixed frequencies. Specifically, we propose a dynamic factor model that permits exact filtering, and we explore the efficacy of our methods both in a...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

This short report deals with the recent rise of programmatic time series methods. This decade has witnessed the proliferation of commercial and open source time-series tooling, which calls for an exposition of what is publicly available. In tandem with this survey, AtsPy, an open source...

We use the monetary model of exchange rate determination to generate in-sample and out-of-sample forecasts of the Rupee-Dollar exchange rate. The assumptions of flexible prices and maintenance of Purchasing Power Parity implies that the domestic price level and the exchange rate are endogenously...

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...

The purpose of this paper is to investigate whether a dynamic Value at Risk model and high frequency realized volatility models can improve the accuracy of 1-day ahead VaR forecasting beyond the performance of frequently used models. As such, this paper constructs 60 conditional volatility...

For several decades, theorists and researchers have emphasized investigating and analyzing the likelihood of contagion of turbulence among markets. The behavior of the housing market is also essential because of the impact of housing prices on the loan portfolios of banks and other financial...

This paper attempts to examine, compare and forecast the per capita GDP of India, the USA, China, and Japan for a period of ten years from 2020 to 2029. It studies the concept of economic convergence which states that the developing economies’ per capita income levels tend to move at a faster...