Jump Dynamics And Volatility Components For OECD Stock Returns

The paper applies Markov Regime Switching Model (MRSM) to investigate the volatility behaviour of twelve OECD stock markets (U.S.A, France, Ireland, Netherlands, Spain, Denmark, Norway, Sweden, Switzerland, UK, Australia and Japan) for the period 2004-2010. The results highlight two different regimes: the first regime consist of low mean high volatility whereas the second regime is categorized by high mean low volatility. We conclude that the periods of high volatility are generally synchronous to several economic and/or political events in all the developed markets during the period under investigation.

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Volatility in stock returns for new EU member states: Markov regime switching model

International Review of Financial Analysis ◽

10.1016/j.irfa.2007.03.006 ◽

2007 ◽

Vol 16 (3) ◽

pp. 282-292 ◽

Cited By ~ 33

Author(s):

Tomoe Moore ◽

Ping Wang

Keyword(s):

Stock Returns ◽

Regime Switching ◽

Member States ◽

Markov Regime Switching ◽

Regime Switching Model ◽

Switching Model ◽

Eu Member States ◽

Markov Regime Switching Model ◽

New Eu Member States

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A CONDITIONAL MARKOV REGIME SWITCHING MODEL TO STUDY MARGINS: APPLICATION TO THE FRENCH FUEL RETAIL MARKETS

Energy Studies Review ◽

10.15173/esr.v21i2.2771 ◽

2015 ◽

Vol 21 (2) ◽

Cited By ~ 1

Author(s):

RAPHAËL HOMAYOUN BOROUMAND ◽

STÉPHANE GOUTTE ◽

SIMON PORCHER ◽

THOMAS PORCHER

Keyword(s):

Markov Chain ◽

Market Structure ◽

Regime Switching ◽

Markov Regime Switching ◽

Regime Switching Model ◽

Switching Model ◽

The Standard Model ◽

High Volatility ◽

Volatility Regimes ◽

Markov Regime Switching Model

This paper uses a regime-switching model that is built on mean-reverting and local volatility processes combined with two Markov regime-switching processes to understand the market structure of the French fuel retail market over the period 1990-2013. The volatility structure of these models depends on a first exogenous Markov chain, whereas the drift structure depends on a conditional Markov chain with respect to the first one. Our model allows us to identify mean reverting and switches in the volatility regimes of the margins. In the standard model of cartel coordination, volatility can increase competition. We find that cartelization is even stronger in phases of high volatility. Our best explanation is that consumers consider volatility in prices to be a change in market structure and are therefore less likely to search for lower-priced retailers, thus increasing the market power of the oligopoly. Our findings provide a better understanding of the behavior of oligopolies.

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An Empirical Study of the Effect of Investor Sentiment on Returns of Different Industries

Mathematical Problems in Engineering ◽

10.1155/2014/545723 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Chuangxia Huang ◽

Xin Yang ◽

Xiaoguang Yang ◽

Hu Sheng

Keyword(s):

Stock Returns ◽

Regime Switching ◽

Investor Sentiment ◽

Regime Switching Model ◽

Switching Model ◽

Current Period ◽

Lag Period ◽

Markov Regime Switching Model ◽

The Relationship ◽

Positive Effect

Studies on investor sentiment are mostly focused on the stock market, but little attention has been paid to the effect of investor sentiment on the return of a specific industry. This paper constructs a proxy variable to examine the relationship between investor sentiment and the return of a specific industry, using the Principle Component Analysis, and finds that investor sentiment is positively correlated with the industry return of the current period and negatively correlated with that of one lag period; we classify investor sentiment as optimistic state and pessimistic state and find that optimistic investor sentiment has a positive effect on stock returns of most industries, while pessimistic investor sentiment has no effect on them; this paper further builds a two-state Markov regime switching model and finds that sentiment has different effect on different industries returns on different states of market.

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