Paper: US mercado de viviendas, integración y contagio

Integration and Contagion in US Housing Markets

Abstract:

This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics.

A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step.

The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.

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Paper: #in Merton Model

Marking systemic portfolio risk with the Merton model

The downside risk of a portfolio of assets is generally substantially higher than the downside risk of its components. In times of crisis, when assets tend to have high correlation, the understanding of this difference can be crucial in managing the systemic risk of a portfolio.
In this article, Alex Langnau and Daniel Cangemi generalise Merton’s option formula in the presence of jumps to the multi-asset case. The methodology provides a new way to mark and risk-manage the  systemic risk of portfolios in a systematic way.

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A esta lectura, le sumaria este paper del 2006.

Paper: VaR y correlación

Implied correlation from VaR

Abstract

Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events in the left tails (crashes) than in the right tails (booms).

 

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Paper: Teoria de matrices aleatorias y correlación

The fine structure of spectral properties for random correlation matrices: an application to financial markets

Abstract

We study some properties of eigenvalue spectra of financial correlation matrices. In particular, we investigate the nature of the large eigenvalue bulks which are observed empirically, and which have often been regarded as a consequence of the supposedly large amount of noise contained in financial data. We challenge this common knowledge by acting on the empirical correlation matrices of two data sets with a filtering procedure which highlights some of the cluster structure they contain, and we analyze the consequences of such filtering on eigenvalue spectra. We show that empirically observed eigenvalue bulks emerge as superpositions of smaller structures, which in turn emerge as a consequence of cross-correlations between stocks. We interpret and corroborate these findings in terms of factor models, and and we compare empirical spectra to those predicted by Random Matrix Theory for such models.

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Paper: Dependencia estadistica del S&P

A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market

Abstract

We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange’s TAQ database. With a copula-based approach, we find that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution,which is implied in the calculation of many correlation coeffcients. We compare the tail dependence to the market’s average correlation level as a commonly used quantity and disclose an neraly linear relation.

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Paper: Correlación en tiempos de crisis

Correlation of financial markets in times of crisis

Abstract

Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investigate several financial market crises that occurred in the years 1987 (Black Monday), 1989 (Russian crisis), 2001 (Burst of the dot-com bubble and September 11), and 2008 (Subprime Mortgage Crisis), which mark some of the largest downturns of financial markets in the last three decades.

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Frase del Día: Cuando todo sube…

“Lesson #1: Investors don’t mind a correlation of one across asset classes when everything goes up.”

(Fuente: EconomicPic)

Paper: Correlación y el SPY

Intraday Correlation Patterns between the S&P 500 and Sector Indices

Abstract:
In this brief research note, I explore recent patterns in intraday return and volume correlation between the S\&P 500 and sector indices, as represented by minutely data from Aug. 23 to Sep. 10 for the SPDR exchange-traded funds. Notably, there appears to be evidence of two previously unreported patterns in intraday correlation. First, there is a “U-shaped” trend in return correlation, characterized by higher correlation at open and close and lower correlation during mid-day hours. Second, volume correlation is marked by lower values in the morning and increasing values in the afternoon. In some cases, this trend even takes the infamous “hockey-stick” shape, exhibiting stable values in the morning but sharply increasing values in the late afternoon. To ensure that these patterns are not a function of the choice of correlation window size, I confirm that these patterns are qualitatively stable over correlation windows ranging from 10 minutes to 90 minutes. These findings indicate that non-time-stationary patterns exist not only for volume and volatility, as previously reported, but also for the correlation of return and volume between the market and sector indices. These results have possible implications for intraday market efficiency and for trading strategies that rely on intraday time-stationarity of return or volume correlation.

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Paper: Correlación y Diversificación

Is the Potential for International Diversification Disappearing?

Abstract:
Since understanding and quantifying the evolution of security co-movements is critical for asset pricing and portfolio allocation, we investigate patterns and trends in correlations over time using weekly returns for large systems of developed markets (DMs) and emerging markets (EMs) during the period 1973-2009. We use the DECO, DCC, and BEKK correlation models, and develop a novel dynamic t-copula which generalizes the normal copula, to allow for dynamic tail dependence. We demonstrate that it is possible to overcome the well known dimensionality problems and compute correlation and tail dependence in international markets using large samples, without relying on factor models. Our results suggest that correlations have been significantly trending upward for both the DMs and EMs. Further, the evidence clearly contradicts the decoupling hypothesis. Although the tail dependence is increasing through time for both EMs and DMs, the level of the tail dependence is still very low at the end of our sample period for EMs as compared to DMs. Therefore, while the correlation analysis suggests that the diversification potential of EMs has largely disappeared, this is contradicted by our findings on tail dependence. Thus, even though diversification benefits might have lessened in the case of DMs, the case for EMs remains intact.

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