Identification of short-term and long-term time scales in stock markets and effect of structural break

The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series...

Full description

Saved in:
Bibliographic Details
Published inIDEAS Working Paper Series from RePEc
Main Authors Mahata, Ajit, Debi Prasad Bal, Nurujjaman, Md
Format Paper
LanguageEnglish
Published St. Louis Federal Reserve Bank of St. Louis 01.01.2019
Subjects
Decomposition
Securities markets
Stock exchanges
Time series
Online AccessGet full text

Cover

More Information
Summary:The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time series before structural break (TSB) and time series after structural break (TSA). The empirical mode decomposition based Hurst exponent and variance techniques have been applied to the TSO, TSB and TSA to identify the time scales in short-term and long-term from the decomposed intrinsic mode functions. We found that for TSO, TSB and TSA the short-term time scales and long-term time scales are within the range of few days to 3 months and greater than 5 months respectively, which indicates that the short-term and long-term time scales are present in the stock market. The Hurst exponent is $\\sim 0.5$ and $\\geq 0.75$ for TSO, TSB and TSA in short-term and long-term respectively, which indicates that the market is random in short-term and strongly correlated in long-term. The identification of time scales at short-term and long-term investment horizon will be useful for investors to design investment and trading strategies.