Sofia Ramos

The Size and Structure of the World Mutual Fund Industry

This paper analyses the mutual fund industry for 20 countries using a new database of more than 50,000 mutual funds. The results suggest that more developed industries provide more benefits to investors as they diversify more internationally, charge lower annual charges and present more product sophistication. The results also have important policy implications by emphasising the role of competition and contestability in industry development. Fewer barriers to entry are positively associated with a larger industry, and concomitantly with more efficiency in terms of returns and fees.

Clustering financial time series: New insights from an extended hidden Markov model

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and time-varying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.

Mixture Hidden Markov Models in Finance Research

Finite mixture models have proven to be a powerful framework whenever unobserved heterogeneity cannot be ignored. We introduce in finance research the Mixture Hidden Markov Model (MHMM) that takes into account time and space heterogeneity simultaneously. This approach is flexible in the sense that it can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity. This methodology is applied to model simultaneously 12 time series of Asian stock markets indexes. Because we selected a heterogeneous sample of countries including both developed and emerging countries, we expect that heterogeneity in market returns due to country idiosyncrasies will show up in the results. The best fitting model was the one with two clusters at country level with different dynamics between the two regimes.

Dynamic clustering of energy markets: An extended hidden Markov approach

This paper studies the synchronization of energy markets using an extended hidden Markov model that captures between- and within-heterogeneity in time series by defining clusters and hidden states, respectively. The model is applied to U.S. data in the period from 1999 to 2012. While oil and natural gas returns are well portrayed by two volatility states, electricity markets need three additional states: two transitory and one to capture a period of abnormally high volatility. Although some states are common to both clusters, results favor the segmentation of energy markets as they are not in the same state at the same time.

Mutual Fund Industry Competition and Concentration: International Evidence

This paper examines mutual fund industry competition and concentration in 27 countries using a sample of almost 50,000 mutual funds. The indicators show that the mutual fund industry is concentrated worldwide and some industries present large fund complexes. Countries with common law and higher stock market turnover are associated with low level industry concentration. There is more industry contestability in countries with better quality of institutions and where regulation is more open. Bank concentration and simultaneous restrictions to engage new activities in the financial industry tend to decrease firm entry in the mutual fund industry. The launch of new funds is positively related with openness of regulation and negatively related with industry age. The overall level of fees tend to be higher in countries with low stock market turnover, where industry size is smaller and where foreign mutual fund companies have a larger market share. Moreover, fund proliferation seems to be an important aspect of competition as it is negatively related with mutual fund fees. Overall, the results do not indicate a direct relation between competition and concentration, similar to the findings for the banking industry. Nevertheless, our evidence validates the contention that the degree of competition is important for the variety of products as we find a larger offer of funds in more competitive industries.

The Interrelationship between Financial and Energy Markets

In the last decade, energy markets have developed substantially due to the growing activity of financial investors. One consequence of this massive presence of investors is a stronger link between the hitherto segmented energy and financial markets.

Risk Factors in the Oil Industry: An Upstream and Downstream Analysis

In this paper we examine the drivers of stock market value in the upstream (producers) and downstream segments (petroleum refiners) of the oil industry.Using a sample of U.S. firms we find that stock returns of upstream and downstream firms follow stock market and oil price returns. Moreover, the upstream firm stock returns are sensitive to changes in the Canadian dollar, an important oil trade partner of the U.S., to natural gas returns and its volatility, but not to oil return volatility.Both the upstream and downstream segments present asymmetric changes regarding oil return changes. Stock returns of the oil industry respond asymmetrically to oil returns, i.e., positive oil returns had a greater impact than oil price drops in the period 1998-2004. Before 1997 we do not find any asymmetric effects, and after 2004, they are only statistically significant in the upstream segment. Overall, the evidence for asymmetric effects is more consistent across measures and time in the upstream than in the downstream segment.

Do Investors Price Industry Risk? Evidence from the Cross-Section of the Oil Industry

Recent research identifies several industry-related patterns that standard asset pricing models cannot explain effectively. This paper investigates what explains the cross-section of returns of firms in the oil industry and, in particular, how well an oil factor performs in comparison with the common systematic factors identified in the literature. We conduct a time series analysis and demonstrate that the oil factor has substantial explanatory power over traditional factors. A cross-sectional regression shows that the size, momentum and oil factors are associated with a positive risk premium and are able to explain the cross-sectional variation in stock returns in the oil industry. Our results suggest that investors demand compensation for the exposure to oil price changes, which has implications for the computation of the cost of equity.

Competition and stock market development

Previous research has found great disparity in growth rates of stock markets supporting the idea that the ranking in financial development is volatile. This paper analyzes the development of stock markets in the last decades and attempts to explain why countries change their ranking in financial development. For that purpose, I analyze 101 stock markets from 1975 to 2003. I find that the divergence is mainly explained by changes in law and regulation enhancing competition. In addition, the general level of competition is positively related with stock market development. Competition causes a decrease in the transaction costs and the cost of capital, driving more firms to list and more traders to trade. Results are consistent through time.

The cyclical behaviour of commodities

Commodities are known to exhibit cyclical behaviour. This paper studies the dynamics of commodities regimes and their implications for portfolio diversification. Using an extension of the regime-switching model, we find that the 12 commodities studied can be clustered into four groups with different regime dynamics, demonstrating that the asset class behaviour of commodities is far from homogeneous. The existence of two regimes is transversal to the assets studied. One regime is marked by high volatility and the other by low volatility. In both regimes, most of the commodities exhibit returns that are not statistically significantly different from those of the stock market regime. The exceptions are oil and natural gas during the low-volatility regime. The analysis of regime synchronization shows that our stock market proxy has low synchronization with commodities, which suggests potential diversification value from adding commodities to an equity portfolio. Based on portfolio optimization, we find that commodities are included in the optimal portfolios in the bull and bear regime of the Standard & Poor’s 500 index. The benefits of diversifying into commodities are particularly strong in the bear stock market regime.