Heterogeneous hidden Markov models (original) (raw)

Modeling of stock indices with HMM-SV models

Theoretical and Applied Economics, 2017

The use of volatility models to conduct volatility forecasting is gaining momentum in empirical literature. The performance of volatility persistence, as indicated by the estimated parameter φ, in Stochastic Volatility (SV) model is typically high. Since future values in SV models are based on the estimation of the parameters, this may lead to poor volatility forecasts. Furthermore, this high persistence, according to some research scientists, is due to the structure changes (e.g. shift of volatility levels) in the volatility processes, which SV model cannot capture. Hidden Markov Models (HMMs) allow for periods with different volatility levels characterized by the hidden states. This work deals with the problem by bringing in the SV model based on Hidden Markov Models (HMMs), called HMM-SV model. Via hidden states, HMMs allow for periods with different volatility levels characterized by the hidden states. Within each state, SV model is applied to model conditional volatility. Empir...

An introduction to the use of hidden Markov models for stock return analysis

2015

We construct two HMMs to model the stock returns for every 10-day period. Our first model uses the Baum-Welch algorithm for inference about volatility, which regards volatility as hidden states and uses a mean zero Gaussian distribution as the emission probability for the stock returns. Our second model uses a spectral algorithm to perform stock returns forecasting. We analyze the tradeoffs of these two implementations as well.

Modeling price dynamics and risk Forecasting in Tehran Stock Exchange: Conditional Variance Heteroscedasticity Hidden Markov Models

Iranian Journal of Finance (IJFIFSA), 2023

Volatility and risk measurement are essential parameters in risk management programs that can affect economic activities and public confidence in the stock market. Also, these two are the keys in the studies that connect the stock market, economic growth, and other financial factors. In recent years, due to using hidden Markov models, but heavy tail distribution is unnecessary. The results indicate that the HM-EGARCH-Normal model appropriately assesses volatility and improves market transparency and risk management forecasts. Also, the VaR and CVaR market risk assessment post-tests using Kupiec and DQ tests do not show evidence of overestimation or underestimation.