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Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or "synthetic sectors". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.

A value investing strategy consists of purchasing stocks relatively undervalued to their funda-mental values and selling those relatively overvalued. Finding this kind of companies has been one of the most challenging goals for investors throughout the history. The main objective of this paper is to test the value factor, but not limited to the traditional Price-To-Book ratio, but explor-ing diverse alternatives constructed on different metrics in order to determine if it possible to obtain excess returns relative to the traditional one. In addition, these factors were blended dif-ferent quality factors. First, we tested the so-called high mispricing portfolios, with long posi-tions in value/high quality stocks and short positions in growth/low quality stocks. When blend-ing these portfolios with quality factors, we observe quite an improvement in terms of Sharpe Ratio and maximum drawdowns relative to pure value portfolios. In this case, we see that ex-cluding riskier low-quality stocks reduces the overall risk of the portfolio. Regarding the low mispricing portfolio, the results show that growth/high quality stocks outperform value/low qual-ity stocks. This is consistent with the hypothesis of behavioral-based theories as we see that only undervalued and high-quality stocks generate excess returns. Finally, we test the results against the three-factor Fama-French models, achieving statistically significant alphas in some cases.

Unlike passive management, where investors almost do not buy and sell securities, active management involves a set of trading rules that govern investment decisions regarding mainly market timing. In this paper, we take the basics of active management and the two fund separation approach, to exploit the fact that an investor can switch between the market portfolio and the risk free asset according to the perceived state of the nature. Our purpose is to evaluate if there is an active management premium by testing performance with our own non-conventional multifactor model, constructed with a Hidden Markov Model which depending on the market states signaled by the level of volatility spread. We have documented that effectively, there is present a premium for actively manage the strategies, giving evidence against the idea that "active managers" destroy capital. We then propose the volatility spread as the active management factor into the Carhart's model used to evaluate trading strategies with respect to a benchmark portfolio.

The return dynamics of Argentina's main stock index, the SP Mer.Val., show a high level of volatility, signaling a higher degree of downside risk. To hedge against that specific risk, investors could buy put options. However, the Argentinean capital markets lacks variety of hedging contracts. The basic availability of put options depends on the possibility of short selling the underlying security, i.e. transfer risk to a third party, something not properly developed in the domestic market. Since data processing power has geometrically increased in the last decades and some mathematic formulas that were helpful for calculation had been surpassed by data gathering and processing that helps to find a better estimate when necessary, in this paper we show the point calculating protection against downside risk in the Argentinean stock market, using real data and programming an algorithm to perform calculations instead of resorting the standard Black-Scholes-Merton formulae, by means of a model free approach to acknowledge the issue.

Conventional financial theory considers ex-ante that risk, generally measured by the volatility, has to be appropriately rewarded by expected returns. In modern financial markets, there are countless quantitative and systematic strategies which may test and eventually lead to excess returns when quantified by these conventional stochastic measures. One of them is the momentum effect which denotes an ongoing movement of the prices of financial assets in a certain direction, for a determined time horizon. Colloquially, assets that have performed better in the past tend to do so in the future. The objective of this paper is to test the existence of excess returns from momentum strategies. To do the aforementioned, we test different selection criteria with diverse weighting schemes. Finally, we analyze how is the behavior of equity options on those underlying assets in order to establish a two-way strategy; first performing pure equity option strategies and then blending equity options with index options.