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Related Concept Videos

Correlations02:20

Correlations

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...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Correlation01:09

Correlation

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:
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

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.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...

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Changes in cross-correlations as an indicator for systemic risk.

Zeyu Zheng1, Boris Podobnik, Ling Feng

  • 1Department of Environmental Sciences, Tokyo University of Information Sciences, Chiba 265-8501, Japan. zeyuzheng8@gmail.com

Scientific Reports
|November 28, 2012
PubMed
Summary
This summary is machine-generated.

Systemic risk, a measure of financial interconnectedness, can be predicted by analyzing changes in principle components (PCs) of economic sector indexes. Increased PC1 changes indicate higher systemic risk, signaling a greater likelihood of future financial crises.

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Area of Science:

  • Financial Economics
  • Quantitative Finance
  • Risk Management

Background:

  • The 2008-2012 global financial crisis highlighted the interconnectedness of financial markets.
  • Studies indicate financial crises correlate with increased stock cross-correlations and systemic risk.
  • Understanding systemic risk is crucial for predicting and mitigating financial instability.

Purpose of the Study:

  • To identify a reliable indicator for systemic risk.
  • To investigate the relationship between principle component analysis (PCA) of economic indexes and systemic risk.
  • To assess the predictive power of PCA-derived metrics for financial crisis likelihood.

Main Methods:

  • Analysis of 10 Dow Jones economic sector indexes.
  • Application of principle component analysis (PCA) with short 12-month time windows.
  • Quantification of the rate of increase in principle components, particularly PC1.

Main Results:

  • The rate of increase in principle components, especially PC1, serves as an effective indicator of systemic risk.
  • A larger change in PC1 directly correlates with a higher increase in systemic risk.
  • This metric demonstrates potential for early detection of heightened financial crisis risk.

Conclusions:

  • Changes in principle components of economic sector indexes can effectively signal rising systemic risk.
  • The magnitude of PC1 change is a key indicator of future financial crisis probability.
  • This PCA-based approach offers a novel method for systemic risk assessment and financial crisis forecasting.