Return is a measure of the profit or loss generated by an investment over a specific period of time. It is expressed as a percentage and is calculated by subtracting the initial investment amount from the final investment value and dividing the result by the initial investment amount.

Annualized return is a measure of the average rate of return earned by an investment over a specific period of time, usually one year. It is calculated by taking the total return of an investment over the given period, adjusting it for the number of years, and expressing it as an annual percentage rate. The Compound Annual Growth Rate (CAGR), also known as geometric mean return, is a way to calculate the annualized return of an investment over a period of time. It is a measure of the average rate at which an investment has grown, taking into account the effect of compounding. The CAGR is determined by finding the value that, if compounded at the same rate over the investment horizon, would result in the same final value as the actual investment.

Cumulative return is a measure of the total return generated by an investment over a specific period of time. It reflects the total amount of profit or loss earned by an investor on an investment, taking into account both capital gains and income over the entire holding period.

All three measures are useful for evaluating investment performance, but they provide different perspectives on the investment's performance. Return provides a snapshot of an investment's performance over a specific period, while annualized return provides a standardized measure of the investment's average performance over multiple periods. Cumulative return provides a measure of the total profit or loss generated by the investment over the entire holding period.

Volatility is a statistical measure of the degree of variation or dispersion of returns for a particular investment or portfolio over a given period of time. In finance, volatility is often used as a proxy for risk, as more volatile assets are generally considered riskier than less volatile assets. Volatility can be measured using several different metrics, but one commonly used measure is standard deviation. Standard deviation measures the degree to which returns deviate from their average over a given time period.

Correlation between assets refers to the measure of the relationship between the returns of two or more assets in a portfolio. It is a statistical measure that ranges from -1 to +1, where a correlation of -1 indicates a perfect negative correlation, a correlation of +1 indicates a perfect positive correlation, and a correlation of 0 indicates no correlation between the two assets. When two assets have a positive correlation, it means that the returns of both assets tend to move in the same direction, either up or down. For example, if the returns of two stocks have a positive correlation, it means that when one stock goes up in value, the other stock is also likely to go up in value. Conversely, when one stock goes down in value, the other stock is also likely to go down in value. When two assets have a negative correlation, it means that the returns of both assets tend to move in opposite directions. For example, if the returns of a stock and a bond have a negative correlation, it means that when the stock goes up in value, the bond is likely to go down in value, and vice versa. The degree of correlation between two assets can have important implications for portfolio management. If two assets have a high positive correlation, it may not provide sufficient diversification benefits in a portfolio, as they are likely to move together. In contrast, if two assets have a low or negative correlation, it can help to reduce the overall risk of a portfolio, as they are likely to move in opposite directions, thus providing diversification benefits.

The annualization factor is a number used to convert an investment return or volatility figure from a shorter time period into an annualized rate of return or volatility figure. It is used to compare investments that have different holding periods on a common basis, as well as to estimate the potential returns and risks of an investment over a full year. To calculate the annualization factor based on trading days, we first need to determine the number of trading days in a year for the particular market or asset being analyzed. For example, the New York Stock Exchange has approximately 252 trading days per year.

The annualization factor is useful for comparing the performance of investments that have different holding periods, but it is important to note that it assumes a constant rate of return or volatility over the full year, which may not be the case. Therefore, investors should use annualized figures as a guide and not rely on them exclusively for decision-making.

Maximum drawdown (MDD) is a measure of the largest loss an investment portfolio has experienced from a peak value to a trough value, before it returns to the previous peak. In other words, it is the maximum percentage decline from the highest point of the portfolio to the lowest point.

Maximum drawdown is a useful measure for investors because it provides an idea of the portfolio's risk and potential loss during adverse market conditions. A higher maximum drawdown indicates that the portfolio has a higher risk of experiencing large losses in the future.

Downside deviation is a risk measurement metric that focuses on the volatility of negative returns and it is used to evaluate the downside risk of an investment or portfolio. To calculate downside deviation, first a threshold return is established. This threshold represents the minimum acceptable return or the amount of risk an investor is willing to tolerate. Then, the difference between each negative return and the threshold return is squared, summed and averaged, and the square root of that average is taken to obtain the downside deviation. Downside deviation is a useful metric because it captures the risk of losses, or the volatility of returns that are less than the threshold, while ignoring the volatility of returns that exceed the threshold. It is especially important for investors who are more concerned with protecting their capital against losses than maximizing their returns.

Value at Risk (VaR) and Historical Value at Risk (HVaR) are both risk management tools used in finance to estimate the potential loss of an investment or portfolio.

VaR is a statistical measure that estimates the potential loss of an investment or portfolio over a given time horizon, with a specified level of confidence. VaR is calculated based on historical data and uses statistical methods to estimate the potential loss that may occur within a given time frame and level of confidence.

HVaR, on the other hand, is based solely on historical data. It estimates the potential loss of an investment or portfolio over a given time horizon, based on the worst-case scenario loss observed in the past. HVaR provides a more conservative estimate of risk, as it assumes that future market conditions will be similar to those in the past.

The Modified VaR is an extension of the traditional VaR calculation that takes into account the skewness and kurtosis of the distribution of returns, which can be important in portfolios with non-normal return distributions. The modified VaR adjusts for these characteristics of the distribution by applying a correction factor to the traditional VaR calculation.

At a 95% level of confidence, VaR provide an estimate of the potential loss that may occur within a certain time frame with 95% certainty. VaR at 95% confidence level means that there is a 5% chance that the actual loss may exceed the estimated VaR. For example, if a portfolio has a 95% VaR of $10,000 over the next month, this means that there is a 5% chance that the actual loss may be greater than $10,000.

Overall, both VaR and HVaR are important risk management tools used by investors and portfolio managers to evaluate the potential downside risk of an investment or portfolio. VaR is a more commonly used tool due to its flexibility and ability to take into account changing market conditions, while HVaR is useful for identifying the worst-case scenario based on past performance.

Expected Shortfall (ES) is a risk management tool used in finance to estimate the potential losses of an investment or portfolio beyond the Value at Risk (VaR) estimate, assuming that the loss exceeds the VaR threshold. ES is also known as Conditional Value at Risk (CVaR) or Tail VaR.

Historical Expected Shortfall (HES) is based on historical data and estimates the average potential loss that may occur beyond the VaR threshold. HES is calculated based on the average of the losses that exceed the VaR threshold observed in the past.

Both ES and HES provide investors and portfolio managers with a more comprehensive estimate of potential losses, taking into account the tail risk beyond the VaR estimate. For example, if the VaR at a 95% confidence level for a particular investment or portfolio is $10,000 over the next month, the ES or HES estimate would provide an estimate of the potential losses that exceed this threshold, assuming that they occur.

Overall, ES and HES are useful risk management tools that provide a more complete picture of potential losses, especially in situations where the VaR estimate may not be sufficient. However, like VaR and HVaR, ES and HES are estimates and not guarantees, and actual losses may exceed these estimates.

The Sharpe ratio is a risk-adjusted performance metric that measures the return generated by an investment per unit of risk taken and used to evaluate the performance of an investment or a portfolio. The Sharpe ratio is calculated by dividing the average Return by the standard deviation of the investment or portfolio's returns. Sharpe Ratio = Average Return / Standard Deviation of Returns A higher Sharpe ratio indicates a better risk-adjusted performance, as it means that the investment or portfolio has generated higher returns for the amount of risk taken. Conversely, a lower Sharpe ratio indicates that the investment or portfolio has generated lower returns for the amount of risk taken.

The Calmar Ratio is a risk-adjusted performance metric that compares an investment's average return to its maximum drawdown over a specific period of time. The Calmar Ratio is calculated by dividing the average return of an investment by its maximum drawdown, which is the maximum percentage decline from the investment's highest value.

Calmar Ratio = Average Return / Maximum Drawdown

A higher Calmar Ratio indicates a better risk-adjusted performance, as it means that the investment has generated higher returns relative to the size of its drawdowns. Conversely, a lower Calmar Ratio indicates that the investment has generated lower returns relative to the size of its drawdowns.

The Sterling Ratio is a risk-adjusted performance metric that measures the average return generated by an investment per unit of downside risk taken. The Sterling Ratio is calculated by dividing the average return of the investment or portfolio by its average drawdown. Sterling Ratio = Average Return / Average Drawdown A higher Sterling Ratio indicates a better risk-adjusted performance, as it means that the investment or portfolio has generated higher returns relative to the size of its downside risk. Conversely, a lower Sterling Ratio indicates that the investment or portfolio has generated lower returns relative to the size of its downside risk.

The Omega Ratio is a risk-adjusted performance metric that measures the probability-weighted ratio of gains to losses for an investment or portfolio. The Omega Ratio is calculated by dividing the probability-weighted average of positive returns by the probability-weighted average of negative returns.

Omega Ratio = Probability-Weighted Average of Positive Returns / Probability-Weighted Average of Negative Returns

A higher Omega Ratio indicates a better risk-adjusted performance, as it means that the investment or portfolio has generated higher returns relative to its downside risk. Conversely, a lower Omega Ratio indicates that the investment or portfolio has generated lower returns relative to its downside risk.

The Sortino Ratio is a risk-adjusted performance metric that measures the return generated by an investment or portfolio per unit of downside risk taken. The Sortino Ratio is calculated by dividing the return of the investment or portfolio by its downside deviation,

Sortino Ratio = Average Return / Downside Deviation

A higher Sortino Ratio indicates a better risk-adjusted performance, as it means that the investment or portfolio has generated higher returns relative to the size of its downside risk. Conversely, a lower Sortino Ratio indicates that the investment or portfolio has generated lower returns relative to the size of its downside risk.

The Upside Potential Ratio (UPR) is a risk-adjusted performance metric that measures the potential upside of an investment relative to its potential downside. The UPR is calculated by dividing the expected upside return of an investment by its expected downside return.

UPR = (Expected Upside Return / Expected Downside Return)

A higher UPR indicates a better risk-adjusted performance, as it means that the investment has higher potential upside relative to its potential downside. Conversely, a lower UPR indicates that the investment has lower potential upside relative to its potential downside.

The Tail Ratio measures the ratio of the average returns of the portfolio during extreme negative events (i.e. in the left tail of the return distribution) to the average returns of the portfolio during normal market conditions.

A higher tail ratio indicates that the portfolio performs relatively worse during extreme negative events, which suggests that the portfolio may be more exposed to tail risk. In contrast, a lower tail ratio indicates that the portfolio performs relatively better during extreme negative events, which suggests that the portfolio may be more resilient to tail risk.

Tail ratio is determined by using quantiles of the return distribution. Specifically, the tail ratio is the ratio of the average returns in the lower tail of the distribution to the average returns in the upper tail of the distribution, usually measured at specific quantiles, such as the 5th and 95th percentiles.

In finance, time series stability refers to the consistency and predictability of a financial time series over time.

R-squared, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance in the dependent variable (in this case, the cumulative returns of a time series) that is explained by the independent variable (usually time). In the context of time series analysis, R-squared can be used to estimate the stability of a time series over time.

To determine time series stability using R-squared, a linear regression model is first fitted to the cumulative returns of the time series data. The R-squared value of the linear fit is then calculated. If the R-squared value is close to 1, this indicates that the linear fit explains a large proportion of the variance in the cumulative returns, which suggests that the time series is relatively stable. On the other hand, if the R-squared value is low, this indicates that the linear fit explains only a small proportion of the variance in the cumulative returns, which suggests that the time series may be more unstable.

Return distribution refers to the pattern of returns observed in a particular investment or portfolio over a given period of time. It is a statistical measure of the frequency and magnitude of returns, which can help investors and portfolio managers in evaluating the risk and return characteristics of an investment. Return distribution can be represented graphically using a histogram, which shows the frequency of returns in different ranges, or using a probability density function (PDF), which shows the probability of returns falling within a certain range. Common statistical measures used to describe return distribution include mean, standard deviation, skewness, and kurtosis. These measures can help in evaluating the shape of the return distribution, as well as the degree of variability and asymmetry in the returns. Understanding return distribution is important in managing risk and constructing a well-diversified portfolio that meets the investor's risk and return objectives. It can help in identifying the range of potential returns and the likelihood of experiencing positive or negative returns, which can inform investment decisions and risk management strategies.