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This study models high and low frequency variation in global equity correlations using a comprehensive sample of 43 countries that includes developed and emerging markets, during the period 1995-2008. These two types of variations are modeled following the semi-parametric Factor-Spline-GARCH approach of Rangel and Engle (2008). This framework is ex...
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Citations
... Ang and Kristensen (2011) estimated time-varying betas with non-parametric techniques, proposing a conditional CAPM and multifactor models for book-to-market and momentum decile portfolios. Engle and Rangel (2010) and Rangel and Engle (2012) provided evidence that models with volatility and correlation components outperform single component models. Patton and Verardo (2012) studied the information flow and its impact on the betas, and found that these increase on announcement days by a statistically significant amount. ...
This paper examines the stochastic behaviour of the realized betas in the CAPM model for the ten largest companies in terms of market capitalisation included in the U.S. Dow Jones stock market index. Fractional integration methods are applied to estimate their degree of persistence at daily, weekly, and monthly frequencies over the period July 2000–July 2020 over time spans of 1, 3, and 5 years. On the whole, the results indicate that the realized betas are highly persistent and do not exhibit weak mean-reverting behaviour at the weekly and daily frequencies, whilst there is some evidence of weak mean reversion at the monthly frequency. Our findings confirm the sensitivity of beta calculations to the choice of frequency and time span (the number of observations).
... Under this framework, the high frequency correlation mean-reverts to a slow-moving low frequency correlation component. The model of Rangel and Engle (2009) assumes a factor asset pricing structure to characterize asset returns. In the present case, I consider the factor specification of Lustig, Roussanov, and Verdelhan (2009). ...
... 5 The approach used in this paper can be modified to incorporate higher frequency data . For example , Engle and Rangel ( 2009 ) synchronize daily returns and estimate the Factor - Spline - GARCH model to characterize correlations dynamics in international equity markets . on model - free rolling correlations motivates the importance of introducing different types of dynamics into these components to explain the variation in the comovement of currency returns over time . ...
... However , the empirical examination of these effects requires further considerations about the underlying assumptions in ( 1 ) and ( 2 ) . Indeed , changes in correlations can be due to other effects that are not captured in specification ( 2 ) , for example , omitted factors and the conditional interactions across both factors and idiosyncratic terms ( see Rangel and Engle ( 2009 ) ) . These effects are incorporated in the specification of the conditional high frequency correlation component , described later in this section . ...
This paper models high and low frequency dynamic components of FX excess return correlations and examines their relationship with economic fundamentals. A factor currency pricing model with time-varying factor loadings is used to characterize the correlation structure of FX excess returns. Aggregate FX comovement is countercyclical as it is positively related to U.S. economic policy uncertainty and negatively related to global GDP growth. The level of comovement is lower in developing countries due to differentiated patterns in their idiosyncratic volatilities. Indeed, country-specific inflation levels and real output growth only impact idiosyncratic volatilities in developing economies. Monetary policy (rates), trade and capital flows affect all currencies.
... As a consequence, there have been a number of attempts that have sought systematic relationships between the stock price index and macroeconomic indicators, either successful or unsuccessful. For example, Engle and Rangel (2009) show the relationship between low frequency volatility in nearly fifty countries' equity markets and the macroeconomic indicators of GDP, inflation, and short-term interests. Chen et al. (1986) test the effects of interest rates, inflation, industrial production, and the spreads between high-and low-grade bonds on stock market returns. ...
It is common knowledge that the more prices deviate from fundamentals, the more likely it is for prices to reverse. Taking this into account, we propose a simple statistical model to identify speculative bubbles in financial markets. Through the estimates of the time varying parameters, including transition probabilities, we can identify when and how newly born bubbles grow and burst over time. The model can be estimated by recursive computations, which require a huge storage capacity for standard computers. For this reason, we introduce an approximation in the computation, maintaining the recursive nature of our estimation technique. We then apply this model to the stock markets of the United States, Japan, and China, estimate its parameters and the probabilities of a bubble crash, and obtain several interesting results: the time series data of the stock price bubble show an inherently non-stationary development and the probability of a bubble crash indeed increases as the stock price becomes too high or too low.
... For example, Rangel and Engle (2012) and Engle and Rangel (2010) provide empirical evidence that a model with low and high frequency volatility and correlation components captures the dynamics of returns in equity markets better than a single component model. This, in turn, suggests that betas themselves might have more than one component. ...
... Secondly, Harvey (2001) shows that the approach based on instruments is relatively sensitive to the set of instruments used. We use a data-driven non-parametric approach to capture time variation in betas rather than parametric and instrument-based approaches such as those in Harvey (1989), Shanken (1990), Ferson and Harvey (1991, Cochrane (1996), Jagannathan andWang (1996), Ang andChen (2007), Boguth, Carlson, Fisher, andSimutin (2011) andRangel andEngle (2012). We do not consider more than three components to beta, so to ensure that we have a parsimonious specification. ...
This paper demonstrates that a conditional version of the Capital Asset Pricing Model (CAPM) explains the cross section of expected returns, just as well as the three factor model of Fama and French. This is achieved by measuring beta (systematic risk) with short-, medium- and long-run components. The short-run component of beta is computed from daily returns over the prior year. While the medium-run beta component is from daily returns over the prior 5 years and the long-run component from monthly returns over the prior 10 years. More immediate changes in risk such as changes in portfolio characteristics are captured in the short-run beta component, whereas, more slowly changing risk due to the business cycle is captured in the medium- and long-run beta components.
... In particular, nonsynchronous trading can introduce spurious lagged spillovers even when markets are independent. To address this issue, we follow Burns, Engle, and Mezrich (1998) and Engle and Rangel (2009) and compute estimates for the prices when markets are closed, conditional on information from markets that are open. We synchronize the data before proceeding to estimate the models described in the previous section. ...
This paper examines the dynamics of volatility across major global exchanges for corn, wheat and soybeans in the USA, Europe
and Asia. We follow a multivariate GARCH approach and account for the potential bias that may arise when considering exchanges
with different closing times. The results indicate that agricultural markets are highly interrelated and there are both own-
and cross-volatility spillovers and dependence among most of the exchanges. In particular, Chicago plays a major role in terms
of spillover effects over other markets. Additionally, the level of interdependence between exchanges has only increased in
recent years for some commodities.
