2014
DOI: 10.1016/j.jempfin.2014.03.007
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Quantiles of the realized stock–bond correlation and links to the macroeconomy

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Cited by 49 publications
(21 citation statements)
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“…Similarly to Rua (2010), stock and bond price indices are converted to the monthly returns by taking the first difference of the natural log for each stock and bond price index. The use of monthly frequencies is commonly used in the literature (see e.g., Kim and In, 2007;Aslanidis and Christiansen, 2014) and is due to the fact that data on macroeconomic factors used in further analysis are available only on a monthly level. The source of the data is Thomson Reuters Datastream.…”
Section: Stock and Bond Market Returnsmentioning
confidence: 99%
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“…Similarly to Rua (2010), stock and bond price indices are converted to the monthly returns by taking the first difference of the natural log for each stock and bond price index. The use of monthly frequencies is commonly used in the literature (see e.g., Kim and In, 2007;Aslanidis and Christiansen, 2014) and is due to the fact that data on macroeconomic factors used in further analysis are available only on a monthly level. The source of the data is Thomson Reuters Datastream.…”
Section: Stock and Bond Market Returnsmentioning
confidence: 99%
“…In contrast, Baele et al (2010) argue that macroeconomic factors play only a minor role in explaining stock-bond correlations in the US market. A more recent study by Aslanidis and Christiansen (2014) provides new insights into the role of macroeconomic fundamentals in explaining stock-bond correlations. They find that macroeconomic factors have only little explanatory power when the stock-bond correlation is largely positive; but when the stock-bond correlation is largely negative, then macroeconomic fundamentals are most useful explanatory variables.…”
Section: Introductionmentioning
confidence: 99%
“…Yang, Zhou, and Wang (2009), by using a large time span that covers 150 years of data at a monthly frequency, recognize that higher stock–bond correlations tend to follow higher short rates or higher inflation rates. Aslanidis and Christiansen (2014) find that macroeconomic fundamentals are the most useful explanatory variables when the stock–bond correlation is largely negative, while Dimic et al (2016) argue that the most important factor influencing stock–bond correlation in the short‐term is the monetary policy; whereas in the long‐term, inflation and stock market uncertainty are the major drivers. Christopher, Kim, and Wu (2012) stress the importance of sovereign credit ratings on time‐varying stock and bond market correlations, while Chiang et al (2015) find evidence that stock–bond correlations are negatively correlated with stock market uncertainty, as measured by the conditional variance and the implied volatility of the S&P 500 index, but positively related to bond market uncertainty.…”
Section: Literature Reviewmentioning
confidence: 99%
“…This approach is similar in spirit to earlier work on the stock-bond correlation quantiles by Aslanidis and Christiansen (2014). Another important di¤erence to Baele, Bekaert, Inghelbrecht, and Wei (2015) is that we also investigate the risk-return trade-o¤ in stocks accounting for the ‡ight-tosafety.…”
Section: Introductionmentioning
confidence: 91%