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Related Concept Videos

Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Scatter Plot01:15

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

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Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Related Experiment Video

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Interplay between past market correlation structure changes and future volatility outbursts.

Nicoló Musmeci1, Tomaso Aste2,3, T Di Matteo1,2

  • 1Department of Mathematics, King's College London, The Strand, London, WC2R 2LS, UK.

Scientific Reports
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

Changes in market correlation structure predict future market volatility. New methods quantify this relationship, offering improved portfolio risk forecasting and overcoming limitations of traditional tools for large asset portfolios.

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Area of Science:

  • Quantitative Finance
  • Network Science
  • Econometrics

Background:

  • Market volatility is a key indicator of financial risk.
  • Understanding the relationship between market correlations and volatility is crucial for risk management.
  • Traditional econometric tools face challenges with high-dimensional financial data.

Purpose of the Study:

  • To investigate the relationship between past changes in market correlation structure and future market volatility.
  • To develop and validate novel methods for anticipating market risk variations.
  • To overcome the curse of dimensionality in financial forecasting models.

Main Methods:

  • Utilizing "correlation structure persistence" on correlation-based information filtering networks to quantify changes in market dependence.
  • Employing "metacorrelation" to measure lagged correlations between correlation matrices over different time windows.
  • Conducting ROC curve analysis to identify optimal parameters and assess forecasting performance.

Main Results:

  • Significant relationships were found between past correlation structure changes and future volatility.
  • Both "correlation structure persistence" and "metacorrelation" effectively anticipate market risk variations.
  • The proposed methods outperform logistic regression models based solely on past volatility.
  • The methods demonstrate robustness and adaptability to abrupt market changes, including financial crises.

Conclusions:

  • Changes in market correlation structure serve as leading indicators for future market volatility.
  • Novel network-based and metacorrelation approaches provide robust and adaptable tools for portfolio risk forecasting.
  • These methods effectively address the curse of dimensionality, enabling analysis of large asset portfolios.