Riskelia’s glossary of systemic risk

Systemic risk:

Systemic risk describes the risk of a system’s implosion due to spillover effects between its components. This risk is related to the density of the connection network between the system’s components. For financial markets, factors increasing systemic risk are leverage, connectedness (liabilities, short term liquidity gaps, OTC derivatives), correlation and concentration of the players’ positions, mass marketing of structured products implying dynamic hedges with positive feedback effects (hedging implies mechanical position shifts that magnify market fluctuations) and pro-cyclical regulation (regulatory capital requirements increase capital needs in times of crisis when the assets’ market value decline and volatility surges ).

Credit risk:

Credit risk refers to the possibility of a default by one or several counterparties in a portfolio of loans or over-the-counter derivatives instruments.

Drawdown:

This risk measure refers to the relative decline in the market value of a risky asset from a “peak” to a “trough” over a certain period of time. Depending on the field of application, the peak can be defined as the all time historical record or the maximum price within a rolling window. This measure quantifies the loss distribution on an asset in the long run.

Financialization:

This concept expresses the growing impact of the financial sphere  on global economic activity. This phenomenon results from the deregulation of financial markets, the globalization of capital flows, the excesses of liquidity coming from macro economic imbalances (trade surplus in emerging countries and oil producers, trade and budget deficit in United States) a growth model based on the accumulation of debt.

Flight to quality

During episodes of panic, investors divert their capital from risky to “safe” assets. This reallocation generally takes place from equity to sovereign high quality bond markets, or from risky to safe countries.

Historical volatility:

This risk measure is the standard deviation of the returns calculated on a rolling time period. The returns can be calculated over different time intervals (daily, weekly,monthly…). To extrapolate the volatility on a different time scale, we use a factor equal to  the square root of time, which is valid under the strong assumption (often incorrect) of independent returns. The volatility is generally expressed in annual terms.

Implied volatility:

This notion has been introduced in financial markets after the emergence of the Black & Scholes (B&S) option pricing formula. It can be derived from the option market price by inverting the B&S formula and reflects the cost of hedging a given market move over a given horizon.

Liquidity risk

Risk of not being able to meet short-term liabilities using the current assets. A funding shortage arises when it is prohibitively expensive both to (i) borrow more funds (low funding liquidity) and (ii) sell off its assets (low market liquidity). In short, problems arise if both funding liquidity dries up (high margins/haircuts, restrained lending) and market liquidity evaporates (fire sale discounts).

Market risk hedging:

Use of financial instruments in order to decrease or neutralize the exposition of a company to market risks: foreign exchange; commodities; financing, and credit risk. Risk hedging usually makes use of derivatives like options and futures (listed instruments) or forwards, swaps and structured products (over the counter instruments).

Mild randomness:

Introduced by Benoit Mandelbrot, this notion refers to a type of random event whose occurrence can be modeled by means of a normal distribution. This definition can be applied more largely to any random phenomenon that can be represented by a more complex statistical distribution, as long as this distribution can be inferred from historical data. Mild randomness is the cornerstone of modern financial theory.

Normal Distribution:

A theoretical frequency distribution, usually represented by a bell-shaped curve, describing a data set concentrated around the mean (top of the bell) with a certain width called standard deviation. The data frequency gets close to zero when moving away from the mean.

Risk:

Risk represents vulnerability to human, reputational or financial losses. Risk is the combination of the likelihood of a random adverse event and its severity.

Risk aversion:

It is the reluctance of market players to invest in risky assets, a feature which commands a risk premium. Risk aversion is correlated to macroeconomic outlook as well as behavioral and collective processes. Risk aversion is also dependant on social and cultural factors.

Risk measures:

Risk measures attempt to assess the gravity of a certain risk. In finance, risk measures may represent a deviation to a mean or an estimate of the potential loss associated to a defined probability.

Risk premium:

It represents the excess return over the risk free rate (usually the yield of the higher quality sovereign short term debt) of an asset or a portfolio of assets. The risk premium depends on the type of assets considered (stock, bonds) and global economic expectations in the markets. High risk premium (resp. low) are observed in a high (resp. low) volatility environment and leads to low  (resp. high) market values of risky financial assets.

Securitization:

This financial technique transforms debt instruments (consumer loans, mortgage debt, corporate bank loans) into market-traded securities. It starts by pooling debts and claims together before transferring them to an ad hoc company (SIV or Special Investment Vehicles, SPV or…, SPC or …) that will market the securities to investors. The securities enable investors to receive the debts and claims capital reimbursement and interest payment following a priority principle based on the claim (or debt) quality. This process has been largely pointed out as one major cause of the subprime crisis due to diluted responsibility in the granting and monitoring of loans, lack of transparency in the quality of initial borrowers and blind trust in valuation model based on hypothetical diversification of default occurrences.

Skewness:

Skewness describes the asymmetry of a distribution. The distribution of  stock price returns present a negative skewness, meaning that large negative returns (or extreme losses) are more frequent than large positive returns.

Solvency risk:

A financial institution is insolvent when the market value of its assets does not exceed the expected value of its liabilities.

Stress test:

This analysis tool aims at measuring the impact of different adverse scenarios  (flight to quality, economic crisis…).

Systematic risk:

Systematic risk is the risk linked to the fluctuations of global market factors (stock indices, volatility, correlation, yield curve drift, funding liquidity…). The intensity is measured by the sensitivity to the considered factor also called the beta.

Value at Risk (VaR):

This risk measure represents a quantile of the distribution of losses related to a financial asset or a global portfolio for a certain time horizon. Thus, the daily VaR 95% represents the losses that may be exceeded in 5% of the cases, so roughly in one out of twenty days.

Wild randomness

This notion, introduced by the mathematician Benoit Mandelbrot, refers to  a type of random phenomenons that display outliers whose importance is comparable in magnitude to the cumulative sum of realizations in “normal” periods. These “black swans” (Nassim Taleb) or “dragon kings” (Didier Sornette)  can take the form of abnormally high daily returns in absolute terms or of large longer-scale price movements (bubbles, crashes, drawdowns).