The January Effect across Volatility Regimes

Using a Markov regime switching model, this article presents evidence on the well-known January effect on stock returns. The specification allows a distinction to be drawn between two regimes, one with high volatility and other with low volatility. We obtain a time-varying January effect that is, in general, positive and significant in both volatility regimes. However, this effect is larger in the high volatility regime. In sharp contrast with most previous literature we find two major results: i) the January effect exists for all size portfolios. ii) the negative correlation between the magnitude of the January effect and the size of portfolios fails across volatility regimes. Moreover, our evidence supports a decline in the January effect for all size portfolios except the smallest, for which it is even larger.Markov Switching Model, Stock Returns, Seasonality, Size Portfolios.

Volatility Regime and Equity Portfolio Return: Evidence from Europe

This paper examines four European equity portfolios sorted by size, book-to-market (B/M) ratios, operating profitability, investment, and momentum by using Markov switching models with high and low volatility regimes. Our empirical analyses derive the following interesting findings. First, in four European equity portfolios, the smallest and the strongest momentum portfolio yields the highest return. In addition, the second smallest and the highest B/M portfolio, the second smallest and the highest operating profitability portfolio, and the second smallest and the second lowest investment portfolio also yield higher returns than the overall equity market in Europe. Further, our analyses using Markov switching models also reveal that for all the four European equity portfolios, the higher returns are obtained not in high volatility regimes but in low volatility regimes, and this evidence is against the assumption of risk-return trade off advocated in standard finance theory. Finally, our Markov switching analyses also suggest that for all the four European portfolios, staying probabilities in the same regimes are high and switching probabilities between two different regimes are generally low. In particular, staying probabilities in low volatility regimes are rather high, thus, all the four European equity portfolios yield high returns very stably by staying high return regimes

Review of Business Intelligence and Portfolios Performance with Case Study

This paper deals with a most important issue that is the effects of business intelligence on portfolios performance, business intelligence can be summarized as the operation to offer the right needs for the right customer which helps the companies' sustainability and continuity also integrity. A study and statistical analysis is performed on some samples of companies and portfolios collected and studied in Jordan. A relationship via business intelligence for both prices discrimination and switching costs and success percent is constructed. Keywords: business intelligence, portfolios, switching costs

Dynamic behavior of value and growth stocks

The difference between the performance of growth and value portfolios presents an interesting puzzle for researchers in finance. Most studies showed that value stocks outperform growth stocks. This is the so-called value premium. In this article, we try to find an answer to the question as to why value stocks generate superior returns to growth stocks by dividing growth and value stocks into switching- and fixed-style stocks. We show that the difference in returns between value and growth stocks is caused by frequently rebalancing portfolios and find a value premium for the switching-style stocks and a growth premium for the fixed-style stocks. We will try to find an explanation for this phenomenon using the behavioral finance explanation that investors are unable to process information correctly. We use earnings announcement return data to test whether expectations of investors about future growth are too extreme.

On the robustness of international portfolio diversification benefits to regime-switching volatility

We examine if the benefits of international portfolio diversification are robust to time-varying asset return volatility. Since diversified portfolios are subject to common cross-country shocks, we focus on the transmission mechanism of such shocks in the presence of regime-switching volatility. Generally, market linkages are stable with little evidence of increased market interdependence in turbulent periods. Furthermore, risk reduction is consistently delivered for the US investor who holds foreign equity.Market comovement; Shift contagion; Financial market crises; International portfolio diversification; Regime switching

International Portfolio Diversification and Market Linkages in the presence of regime-switching volatility

We examine if the benefits of international portfolio diversification are robust to time-varying asset return volatility. Since diversified portfolios are subject to common cross-country shocks, we focus on the transmission mechanism of such shocks in the presence of regime-switching volatility. We find little evidence of incresaed market interdependence in turbulent periods. Furthermore, for the vast majority of time, we show that risk reduction is delivered for the US investor who holds foreign equitMarket comovement, International portfolio diversification, Financial market crises, Regime switching

International Portfolio Diversification and Market Linkages in the presence of regime-switching volatility

We examine if the benefits of international portfolio diversification are robust to time-varying asset return volatility. Since diversified portfolios are subject to common cross-country shocks, we focus on the transmission mechanism of such shocks in the presence of regime-switching volatility. We find little evidence of increased market interdependence in turbulent periods. Furthermore, for the vast majority of time, we show that risk reduction is delivered for the US investor who holds foreign equity.Market comovement; International portfolio diversification; Financial market crises; Regime switching.

The Latin American and Spanish Stock markets

In this article I analyze the Spanish stock market in an international setting. Using a simple Markov regime switching model I get a time varying measure of the effect of the return on a Latin American portfolio on the Spanish stock returns. The evidence can be summarized as follows. First, I find that this effect is positive and no so large. However, it has increased since the mid-nineties. Second, evidence for the returns on size portfolios shows that most of the effect accrues indirectly through common risk factors. The portfolio composes of stocks with small capitalization is the most affected. Nevertheless, the relative effect of the Latin America to the effect of the world only increases for the portfolio composes of stocks with big capitalization since the mid-nineties. Third, evidence for the returns on sector portfolios shows that the most active sectors investing in Latin America are the most affected. Fourth, I conclude that there is no a positive relatio nship between â-risk and flows of foreign direct investment.Markov switching model, maximum likelihood estimation, stock returns.

Risk management and extreme scenario development using multiple regime switching approaches : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Finance at Massey University

Over the last twenty-five years, there have been an increasingly large number of extreme events in the financial markets. This includes market crashes and natural disasters that have led to extremely large losses and claims. Extreme event risk affects all aspects of risk assessment modeling and management. Traditional risk measurement methods focus on probability of laws governing average of sums, and do not focus on the tails of distribution. The investigation concerns the characterization and development of extreme markets scenarios for use in risk measurement and capital adequacy determination frameworks. The first part of the investigation concerns the development of event timelines that can be used for characterizing whether a period of time should be considered normal or extreme market conditions or regimes. The time lines have allowed the identification of the different times when the markets were calm and when the markets were turbulent. They assist in building scenarios, and also to identify the scenarios for decomposition of data to model the different regions, either the tail or the center of the distribution using the mentioned regime switching models. The information from the event time line can be used to define scenarios in a stress testing context. In this investigation, extreme value analysis, which is an extension of the standard VaR techniques, useful in measuring extreme events has been used, which fits density functions by placing more weights in the tails than the normal Gaussian distribution and model the upper and lower tail of an underlying distribution. Extreme value distribution functions including "fat tailed" will be fitted to the tails of critical market factors to model the extreme market events that are not given appropriate probability of occurrence under normal conditions. The Hill estimator, which is recognized as the consistent estimator for empirical analysis is used for calculating the tail index parameter for EVT modeling, However, it has to be noted that the Hill estimator is efficient when the underlying distribution is fat-tailed as compared to the gaussian, where the tail index estimates tend to go to infinity. The performance of Extreme value theory estimation technique with multiple regimes on real and simulated financial time series for efficient results, compared to the standard VaR techniques has been studied. In this investigation, multiple regime switching approach has been used to identify regimes and measure risk accordingly. It is assumed that the center of the returns distribution is normally distributed with 90 percent of the data in the in the center region and each tail contains 5 percent of the data. Three regime switching models have been used in this analysis which includes, the Unconditional LT-C-RT (Left tail - Center - Right Tail) transfers, the 3 State Regime Markov Transition Model and the Geometric Time in Trail Model. The regime switching models are modeled using the following procedures: 1) The Unconditional LT-C-RT (Left Tail - Center - Right Tail) model is an IID model (Independent and Identically Distributed) model and has a simple Bernoulli approach where the market is in a normal state with probability P or an abnormal state with probability 1 - p . The transition between states is independent of the last state. 2) A Markov chain approach where the next state of the market is a function of the current state. That there are the following transitions possible: 2.1) Normal to normal 2.2) Normal to abnormal 2.3) Abnormal to abnormal 2.4) Abnormal to normal 3). The Geometric Time in Tail model is a hybrid Bernoulli approach where the markets stays in a given state based on a duration model and when the duration in a given states has expired, the sampling of the next state using a independent Bernoulli approach similar to approach one. This implies that the after the market has stayed in a given regime for the sample duration time, it can stay in the current regime with probability p or leave the regime with probability 1 - p. The sample duration can be based on the exponential distribution for continuous time and the geometric distribution for discrete time such as daily movements. Tail index estimation results using EVT indicate the presence of fat tails in equity data and the results of Value-at-Risk (VaR) and Expected Shortfall (ES) are considerably similar for the three regime switching models. The comparison of results from the multiple regime switching models to the one region distribution results, which serve as the base case prove the efficiency of using this approach for a better risk measure