Decomposing global yield curve co-movement

Byrne, Joseph P.; Cao, Shuo; Korobilis, Dimitris

doi:10.1016/j.jbankfin.2019.07.018

Cited by 14 publications

(3 citation statements)

References 61 publications

(73 reference statements)

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“…Domowitz et al [ 21 ] proved that liquidity co-movement is determined by order flow and return co-movement is caused by order type (market and limit orders). Byrne et al [ 22 ] found that co-movement is responsible for two-thirds of variability in global bond yields. These authors concluded that global inflation explains most of the global yield co-movement.…”

Section: Introductionmentioning

confidence: 99%

A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics

López-García

Sánchez-Granero

Segovia

et al. 2020

Entropy

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One of the main contributions of the Capital Assets Pricing Model (CAPM) to portfolio theory was to explain the correlation between assets through its relationship with the market index. According to this approach, the market index is expected to explain the co-movement between two different stocks to a great extent. In this paper, we try to verify this hypothesis using a sample of 3.000 stocks of the USA market (attending to liquidity, capitalization, and free float criteria) by using some functions inspired by cooperative dynamics in physical particle systems. We will show that all of the co-movement among the stocks is completely explained by the market, even without considering the market beta of the stocks.

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Section: Introductionmentioning

confidence: 99%

A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics

López-García

Sánchez-Granero

Segovia

et al. 2020

Entropy

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“…There are many possible avenues for future works. A desirable address is to develop a model that includes the international bond yield curve (Byrne et al, 2019), macro variables, and the GDP of countries the United States trade with.…”

Section: Discussionmentioning

confidence: 99%

GDP Forecasting: Machine Learning, Linear or Autoregression?

Maccarrone

Morelli

Spadaccini

2021

Front. Artif. Intell.

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This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.

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“…Others attributed the co-movement to market order flows and order type (Domowitz et al [4]). Byrne et al [5] concluded that global inflation explains most of the global yield co-movement. Some researchers also considered that co-movement is affected by variables that reflect different institutional aspects, such as international macroeconomic policy (Parsley and Popper [6]).…”

Section: Introductionmentioning

confidence: 99%

Volatility Co-Movement in Stock Markets

et al. 2021

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The volatility and log-price collective movements among stocks of a given market are studied in this work using co-movement functions inspired by similar functions in the physics of many-body systems, where the collective motions are a signal of structural rearrangement. This methodology is aimed to identify the cause of coherent changes in volatility or price. The function is calculated using the product of the variations in volatility (or price) of a pair of stocks, averaged over all pair particles. In addition to the global volatility co-movement, its distribution according to the volatility of the stocks is also studied. We find that stocks with similar volatility tend to have a greater co-movement than stocks with dissimilar volatility, with a general decrease in co-movement with increasing volatility. On the other hand, when the average volatility (or log-price) is subtracted from the stock volatility (or log-price), the co-movement decreases notably and becomes almost zero. This result, interpreted within the background of many body physics, allows us to identify the index motion as the main source for the co-movement. Finally, we confirm that during crisis periods, the volatility and log-price co-movement are much higher than in calmer periods.

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Decomposing global yield curve co-movement

Cited by 14 publications

References 61 publications

A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics

A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics

GDP Forecasting: Machine Learning, Linear or Autoregression?

Volatility Co-Movement in Stock Markets

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