gaussian copula造句

• A further criticism of the Gaussian copula is the difficulty to calibrate it to market prices.
• The Gaussian copula is a distribution over the unit cube [ 0, 1 ] ^ d.
• Thus, forecasting with Monte-Carlo simulation with the Gaussian copula and well-specified marginal distributions are effective.
• Quantitative techniques that use Monte-Carlo simulation with the Gaussian copula and well-specified marginal distributions are effective.
• It can be mathematically shown that the Gaussian copula has relative low tail dependence, as seen in the following scatter plots.
• Li's Gaussian copula formula assumed that the price of CDS was correlated with and could predict the correct price of mortgage backed securities.
• Created using Gaussian Copula model and 5, 000 simulationsBespoke portfolios can have very different default correlation characteristics from credit indices with similar distributions of riskiness.
• In section 5.4, he uses Gaussian Copula to measure event relationships, or mathematically, " correlations ", between random economic events, expressed as:
• ;David X . Li : Canadian qualified actuary who in the first decade of the 21st century pioneered the use of Gaussian copula models for the pricing of collateralized debt obligations ( CDOs ).
• For example, to improve the estimation of the Variance Covariance matrix, one can generate a forecast of asset distributions via Monte-Carlo simulation based upon the Gaussian copula and well-specified marginals.
• According to an article published in " Wired ", the risk was further systematized by the use of David X . Li's Gaussian copula model function to rapidly price Collateralized debt obligations based on the price of related credit default swaps.
• In 2010, he also produced and directed " Dreams & Colors ", visionary documentary about Piazza Navona in Rome, and " Gaussian Copula Function " based on Felix Salmon's article published by " Wired " about the disaster of Wall Street ( 2008 ).
• The cracks became full-fledged canyons in 2008 when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril . . . Li's Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees ."
• Some examples of such failures in risk assessment include the use of Gaussian models in finance ( Black-Scholes, the Gaussian copula, LTCM ), the use of Gaussian processes and linear wave theory failing to predict the occurrence of rogue waves, the failure of economic models in general to predict the 2007 financial crisis, and the under-appreciation of external events, cascades, and nonlinear effects in probabilistic risk assessment, leading to not anticipating the 2011 Fukushima disaster.
• Excessive consumer housing debt was in turn caused by the mortgage-backed security, credit default swap, and collateralized debt obligation sub-sectors of the finance industry, which were offering irrationally low interest rates and irrationally high levels of approval to subprime mortgage consumers because they were calculating aggregate risk using gaussian copula formulas that strictly assumed the independence of individual component mortgages, when in fact the credit-worthiness almost every new subprime mortgage was highly correlated with that of any other because of linkages through consumer spending levels which fell sharply when property values began to fall during the initial wave of mortgage defaults.
• Excessive consumer housing debt was in turn caused by the mortgage-backed security, credit default swap, and collateralized debt obligation sub-sectors of the finance industry, which were offering irrationally low interest rates and irrationally high levels of approval to subprime mortgage consumers because they were calculating aggregate risk using gaussian copula formulas that strictly assumed the independence of individual component mortgages, when in fact the credit-worthiness of almost every new subprime mortgage was highly correlated with that of any other because of linkages through consumer spending levels which fell sharply when property values began to fall during the initial wave of mortgage defaults.