Market forecasting using Hidden Markov Models

TL;DR

This paper explores the application of Hidden Markov Models to forecast EUR/USD Futures prices, analyzing their effectiveness in modeling financial time series and comparing existing forecasting methods.

Contribution

It provides an analysis of how HMMs describe financial time series and evaluates different forecasting approaches, highlighting their strengths and weaknesses.

Findings

HMMs effectively model EUR/USD price dynamics.

Comparison reveals advantages and limitations of various forecasting methods.

Insights into the structure of financial time series using HMMs.

Abstract

Working on the daily closing prices and logreturns, in this paper we deal with the use of Hidden Markov Models (HMMs) to forecast the price of the EUR/USD Futures. The aim of our work is to understand how the HMMs describe different financial time series depending on their structure. Subsequently, we analyse the forecasting methods exposed in the previous literature, putting on evidence their pros and cons.