Open AccessStock Daily Price Regime Model Detection using Markov Switching Model
Author(s) -
Wiwik Prihartani,
Dwilaksana Abdullah Rasyid,
Nur Iriawan
Publication year - 2020
Publication title -
matematika
Language(s) - English
Resource type - Journals
eISSN - 0127-9602
pISSN - 0127-8274
DOI - 10.11113/matematika.v36.n2.1189
Subject(s) - akaike information criterion , econometrics , markov chain , stock (firearms) , markov model , economics , markov process , stock market , stock price , mathematics , statistics , engineering , series (stratigraphy) , geography , mechanical engineering , paleontology , context (archaeology) , archaeology , biology
Changes in stock prices randomly occur due to market forces with reoccurrencepossibilities. This process, also known as the structural break model, is captured throughchanges in the linear model parameters among periods with the Markov Switching Model(MSwM) used for detection. Furthermore, using the smallest Akaike Information Criterion(AIC) value on all feasible MSwM alternatives formed for a daily stock price, the completeMSwM model with its Markov transition is determined. This method has been tested andapplied to daily stock price data in several sectors. The result showed that the number ofregime models coupled with its transition probability helped investors make investmentdecisions.