In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a...

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where...

Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a...

I present evidence that higher frequency measures of inflation expectations outperform lower frequency measures of inflation expectations in tests of accuracy, predictive power, and rationality. For decades, the academic literature has focused on three survey measures of expected inflation: the...

In this paper we examine and present the methodology of feed-forward neural networks with error backpropagation algorithm and non-linear methods. We test some applications of time-series analysis in economics. The first part is consisted by applications following the traditional approach of...

A set of multivariate GARCH models is estimated and its empirical validity is compared from the calculation of the Value at Risk. Data used are the daily returns of the nominal exchange rate of the Colombian peso vis-a-vis the American dollar, euro, sterling and Japanese yen for the period...

Duration is an important parameter used by investors to choose between different investment opportunities in financial economics. While the concept of duration is usually associated with fixed-income assets, its expansion to the equity assets is becoming more relevant in the recent period, due...

In this paper we show how IGARCH effects can arise as an artifact of unaccounted structural change. Using daily returns for the DM/US$ and Yen/US$ exchange rates, the finding is shown to have empirical relevance. GARCH models appear to be useful approximations, for short term forecasting, to a...

In this paper we test for the existence of long memory and structural breaks in the realized variance process for the DM/US$ and Yen/US$ exchange rates. While long memory is evident in the actual processes, a structural break analysis reveals that this feature is partially explained by...

Machine Learning algorithms have been widely used and proven effective in financial markets. In this paper, we introduced a Machine Learning model set trained on the residual factors from the Fama-French three-factor model (Fama and French, 1992) to find significant alpha factors. To include...