News, Copulas and Independence

This dissertation contributes to the theory and the applications of copulas to problems in economics, econometrics and finance. The second chapter proposes a new measure of macroeconomic news which is termed the Macroeconomic News Index . Using the copula approach, new findings about the relationship between macroeconomic news and the stock markets are revealed. The third chapter aims to improve the existing non-parametric copula-based tests for stochastic independence. It provides an extension to the test statistic of Kojadinovic and Holmes (2009), which is obtained through the introduction of a weighted functional norm. The addition of the weights creates a channel through which the power properties of the test can be manipulated. Certain choices of the weights are shown to give the statistic a significant power advantage. The third chapter provides additional results which enable the application of the test to regression model residuals. The test is used to probe for the presence of conditional heteroscedasticity, and is shown to have a power advantage over the test of White (1980). The fourth chapter provides a serial extension to the statistic, and further extends the results of Quessy (2010), which permits the application of the statistic to the testing for the goodness of fit of serial copulas. An upper bound for the independence test statistic is derived in Chapter 4, and a standardized version of the statistic is proposed, which can serve as an omnibus measure of vectorial serial dependence. A computational formula for the new copula-based dependence measure is provided

A Money and Credit Real-Time Database for Canada

Model-based forecasts of important economic variables are part of the range of information considered for monetary policy decision making. Since some of the data underpinning these forecasts can be revised over time as new information is released, having access to the data that are available when decisions are made can have a significant impact on assessments of forecasting models. A database of published information for a set of money and credit variables has been developed at the Bank of Canada. This real-time database, which will make available estimates of money and credit data that have been published at different times, is expected to be of great help to researchers developing models based on money and credit data. The authors describe the contents of the new database and discuss patterns in data revisions. While they find that most revisions are unbiased, they provide evidence that revisions to some of the money and credit aggregates are biased. In particular, revisions to long-term business credit and total business credit tend to show an upward bias over longer periods. The authors argue that this may be because there tends to be a delay in factoring the effects of financial innovations into time series. Practitionners should consider this when interpreting developments in business credit.

A New Measure of Vector Dependence, with an Application to Financial Contagion

We propose a new nonparametric measure of association between an arbitrary number of random vectors. The measure is based on the empirical copula process for the multivariate marginals, corresponding to the vectors, and is insensitive to the within-vector dependence. It is bounded by the [0, 1] interval, covering the entire range of dependence from vector independence to a vector version of a monotone relationship. We study the properties of the new measure under several well-known copulas and provide a nonparametric estimator of the measure, along with its asymptotic theory, under fairly general assumptions. To illustrate the applicability of the new measure, we use it to assess the degree of interdependence between equity markets in North and South America, Europe and Asia, surrounding the financial crisis of 2008. We find strong evidence of previously unknown contagion patterns, with selected regions exhibiting little dependence before and after the crisis and a lot of dependence during the crisis period

Могут ли фондовые аналитики предсказать рыночный риск? Новые сведения из теории копулы

We assess investment value of stock recommendations from the standpoint of market risk. We match I/B/E/S (Institutional Brokers’ Estimates System) consensus recommendations issued in January 2015 for a cross-section of u.S. public equities with realized volatility of these papers, showing that these recommendations signifcantly correlate with subsequent changes in market risk. Thus, the results indicate that to some extent the analysts can predict an increase or decrease in risk, which can beneft asset management. However, the relationship between the recommendations and the risk is not linear and depends on the specifc recommendation. using a semi-parametric copula model, we fnd recommendation levels to be associated with future changes in volatility. We further fnd this relationship to be asymmetric and most pronounced among the best-rated stocks which experience largest volatility declines. We conduct a trading simulation showing how stock selection based on such ratings can lead to a reduction in portfolio-level value-at-risk.Статья оценивает способность финансовых аналитиков прогнозировать рыночный риск. Сопоставляя консенсус-рекомендации, выпущенные аналитиками для акций публичных компаний США, содержащихся в системе I/B/E/S (Institutional Brokers’ Estimates System) на январь 2015 г., с фактической волатильностью этих бумаг, мы показываем, что эти рекомендации значимо коррелируют с последующими изменениями в уровне рыночного риска. Таким образом, наши результаты указывают на то, что аналитики хотя бы в какой-то степени способны предсказать нарастание или убывание риска, что может принести пользу в управлении активами. Однако взаимоотношение между рекомендациями и риском не является линейным и зависит от конкретной рекомендации. Используя семи-параметрическую статистическую модель на основе теории копул, автор показывает, что «экстремальные» рекомендации (т.е. самые положительные или самые отрицательные) несут гораздо большую информационную нагрузку, чем остальные. В контексте научной литературы на данную тему результаты исследования, по-видимому, представляют собой одну из первых попыток установить эмпирическую зависимость между рекомендациями аналитиков и рыночным риском

Can Analysts Predict Rallies Better Than Crashes?

We use the copula approach to study the structure of dependence between sell-side analysts' consensus recommendations and subsequent security returns, with a focus on asymmetric tail dependence. We match monthly vintages of I/B/E/S recommendations for the period January to December 2011 with excess security returns during six months following recommendation issue. Using a symmetrized Joe-Clayton Copula (SJC) model we find evidence to suggest that analysts can identify stocks that will substantially outperform, but not underperform relative to the market, and that their predictive ability is conditional on recommendation changes

Can Analysts Predict Rallies Better Than Crashes?

We use the copula approach to study the structure of dependence between sell-side analysts' consensus recommendations and subsequent security returns, with a focus on asymmetric tail dependence. We match monthly vintages of I/B/E/S recommendations for the period January to December 2011 with excess security returns during six months following recommendation issue. Using a symmetrized Joe-Clayton Copula (SJC) model we find evidence to suggest that analysts can identify stocks that will substantially outperform, but not underperform relative to the market, and that their predictive ability is conditional on recommendation changes

An equation-free computational approach for extracting population-level behavior from individual-based models of biological dispersal

The movement of many organisms can be described as a random walk at either or both the individual and population level. The rules for this random walk are based on complex biological processes and it may be difficult to develop a tractable, quantitatively-accurate, individual-level model. However, important problems in areas ranging from ecology to medicine involve large collections of individuals, and a further intellectual challenge is to model population-level behavior based on a detailed individual-level model. Because of the large number of interacting individuals and because the individual-level model is complex, classical direct Monte Carlo simulations can be very slow, and often of little practical use. In this case, an equation-free approach may provide effective methods for the analysis and simulation of individual-based models. In this paper we analyze equation-free coarse projective integration. For analytical purposes, we start with known partial differential equations describing biological random walks and we study the projective integration of these equations. In particular, we illustrate how to accelerate explicit numerical methods for solving these equations. Then we present illustrative kinetic Monte Carlo simulations of these random walks and show a decrease in computational time by as much as a factor of a thousand can be obtained by exploiting the ideas developed by analysis of the closed form PDEs. The illustrative biological example here is chemotaxis, but it could be any random walker which biases its movement in response to environmental cues.Comment: 30 pages, submitted to Physica