Breaking Down Risk Modeling Cheat Sheet by djjang2

Shareh­olders' Equity: represents the net worth of a company, which is the dollar amount that would be returned to shareh­olders if a company's total assets were liquid­ated, and all of its debts were repaid. Typically listed on a company's balance sheet, this financial metric is commonly used by analysts to determine a company's overall fiscal health.
Shareh­olders' equity is also used to determine the value of ratios, such as the debt-t­o-e­quity ratio (D/E), return on equity (ROE), and the book value of equity per share (BVPS).

Variance: The term variance refers to a statis­tical measur­ement of the spread between numbers in a data set. Variance measures how far each number in the set is from the mean and thus from every other number in the set.
The square root of the variance is the standard deviation (σ), which helps determine the consis­tency of an invest­ment's returns over a period of time

Investors use variance to see how much risk an investment carries and whether it will be profit­able.

Capital Asset Pricing Model (CAPM): describes the relati­onship between systematic risk and expected return for assets, partic­ularly stocks.

Derivative: A derivative is a financial security with a value that is reliant upon or derived from, an underlying asset or group of assets—a benchmark. The derivative itself is a contract between two or more parties, and the derivative derives its price from fluctu­ations in the underlying asset.

Skewness: refers to a distortion or asymmetry that deviates from the symmet­rical bell curve, or normal distri­bution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a repres­ent­ation of the extent to which a given distri­bution varies from a normal distri­bution. A normal distri­bution has a skew of zero, while a lognormal distri­bution, for ex, would exhibit some degree of right-­skew.

Volati­lity: a statis­tical measure of the dispersion of returns for a given security or market index. In most cases, the higher the volati­lity, the riskier the security. Volatility is often measured as either the standard deviation or variance between returns from that same security or market index.

Implied volatility: the parameter component of an option pricing model, such as the Black-­Scholes model, which gives the market price of an option. Implied volatility shows how the market­place views where volatility should be in the future.
Since implied volatility is forwar­d-l­ooking, it helps us gauge the sentiment about the volatility of a stock or the market. However, implied volatility does not forecast the direction in which an option is headed.

With Historical Volati­lity, traders use past trading ranges of underlying securities and indexes to calculate price changes.
Calcul­ations for historical volatility are generally based on the change from one closing price to the next.

Monte Carlo Simulation: used to model the probab­ility of different outcomes in a process that cannot easily be predicted due to the interv­ention of random variables.
It is a technique used to understand the impact of risk and uncert­ainty in prediction and foreca­sting models.

Correl­ation Coeffi­cient: a statis­tical measure of the strength of the relati­onship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correl­ation measur­ement. A correl­ation of -1.0 shows a perfect negative correl­ation, while a correl­ation of 1.0 shows a perfect positive correl­ation. A correl­ation of 0.0 shows no linear relati­onship between the movement of the two variables.

Expected Value: A weighted average of the values of the random variable, for which the probab­ility function provides the weights, based on its Theore­tical Probab­ility.
If an experiment can be repeated a large number of times, the expected value can be interp­reted as long-run average

Dispersion: that describes the size of the distri­bution of values expected for a particular variable. Dispersion can be measured by several different statis­tics, such as range, variance, and standard deviation. In finance and investing, dispersion usually refers to the range of possible returns on an invest­ment, but it can also be used to measure the risk inherent in a particular security or investment portfolio. It is often interp­reted as a measure of the degree of uncert­ainty, and thus, risk, associated with a particular security or investment portfolio. {{nl} Generally speaking, the higher the disper­sion, the riskier an investment is, and vice versa.

Regression: a statis­tical method used in finance, investing, and other discip­lines that attempts to determine the strength and character of the relati­onship between one dependent variable (usually denoted by Y) and a series of other variables (known as indepe­ndent variab­les).
Regression helps investment and financial managers to value assets and understand the relati­onships between variables, such as commodity prices and the stocks of businesses dealing in those commod­ities.

Probab­ility Analysis: A technique for foreca­sting events, such as accidental and business losses, on the assumption that they are governed by an unchanging probab­ility distri­bution.

Empirical Probab­ility (Poste­riori Probab­ility): Probab­ility measure that is based on actual experience through historical data or observ­ation of facts.
What is Empirical Probab­ility? Empirical probab­ility uses the number of occurr­ences of an outcome within a sample set as a basis for determ­ining the probab­ility of that outcome. The number of times "­event X" happens out of 100 trials will be the probab­ility of event X happening. An empirical probab­ility is closely related to the relative frequency of an event.

Normal Distri­bution: A balanced probab­ility distri­bution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distri­bution will appear as a bell curve.

Continuous Probab­ility Distri­bution: repres­ented as either graphs or by dividing the distri­bution into a finite # of bins and calcul­ating the probab­ility of an outcome failing within the range repres­ented by each bin; shows all the possible outcomes and associated probab­ilities for a given event.

Probab­ility Analysis: A technique for foreca­sting events, such as accidental and business losses, on the assumption that they are governed by an unchanging probab­ility distri­bution.

Theore­tical Probab­ility: Probab­ility that is based on theore­tical principles rather than real experi­ences (Coin Toss/R­olling Dice)

Law of Large Numbers: A mathematic principle stating that as the number of similar but indepe­ndent exposure units increase, the relative accuracy or predic­tions about future outcomes (losses) also increase.

Value at Risk (VaR): A technique to quantify financial risk by measuring the likelihood of losing more than a specific dollar amount over a specific period of time.

Trend Analysis: An analysis that identifies patterns in past data and then projects these patterns into the future.

Linear Regression Analysis: A form of regression analysis that assumes that the change in the dependent variable is constant for each unit of change in the indepe­ndent variable.