Using Macro Scenario Analysis to Assess Market Stress Losses
Summary
The question asks how to model market-wide price impact during multi-day stress, including changing liquidity, volatility, correlated selling, and possible feedback loops. It contrasts this problem with conventional short-horizon models of individual order impact, which are described as typically concave in order size. The question raises possible explanatory variables and functional forms but does not supply an estimated impact model or empirical evidence for a particular curve.
The response offers a risk-management alternative: define a stress scenario through shocks to macroeconomic and market variables, apply those shocks to the inputs of stochastic or fundamental valuation models, and revalue the portfolio. Portfolio loss under the scenario then serves as a measure of stress exposure. This is a scenario analysis framework rather than a calibrated function mapping aggregate trading volume to price movement. Its usefulness depends on choosing plausible, coherent shocks and suitable valuation models; the brief answer does not explain how to model feedback dynamics or calibrate multi-day impact.
Key ideas
- The question distinguishes market-wide, multi-day stress dynamics from short-horizon impact of individual orders.
- Traditional order impact is described as generally concave in order size under normal conditions.
- Scenario analysis applies prescribed shocks to macro and market variables, then revalues a portfolio.
- Portfolio loss under a stress scenario provides a risk measure without directly estimating a volume-based impact curve.
- The response does not specify how to calibrate correlated selling, liquidity changes, or feedback loops.
Tags
Full text
# Market impact in stress # Market impact in stress I am trying to model the price impact in stress for a period of several days. Specifically, I am looking for a `function/model` that predicts the price movement - Given a set of ex-ante factors (e.g: liquidity, implied volatility, etc.) and - As a function of the volume size (proxy for the intensity of the stress) Price impact models typically assess the impact of a) `individual orders` (including meta-orders), b) `under normal market conditions` and c) `over short horizons` (minutes). Under a) to c), empirical analyses find that the price impact is typically a concave function of order size, progressively smaller market impact as volume grows. In contrast to traditional models, I would like to model the impact of a') `market-wide aggregate dynamics` (including correlated orders / herding behaviors), b') `calibrated under stress` (with potentially reduced liquidity and downward price spirals) and c') `over an horizon of several days` (which reinforce possible negative feedback loops). Could someone give me some ideas regarding: - Which explanatory factors could be used: market depth, implied vol, ….?. - Which models or impact functions: concave, convex, two-step (e.g: concave and convex), others? PS: I am aware that this question is at the boundary between market microstructure and risk management. It would be helpful to hear your views regarding the benefits of extending price impact models to (short) horizons that may be covered by risk management models (e.g: stochastic processes or historical analysis). ## Answer by toing (score 1) https://quant.stackexchange.com/a/60825 I will attempt to elaborate on this from risk management perspective. - scenario analysis approach: An example of this is stress testing that Fed mandates for investment banks. Fed gives stress variables to various fundamental macro variables. For example, a certain market stress scenario will be rates down 100bps, volatility up 30%, curve flatter by 30bps, corporate spreads wider 200bps, High yields wider by 1000bps etc etc. Once you have these macro variable shocks, you just use these inside your stochastic or fundamental models and recompute price of portfolio you own. Based on how much your portfolio can lose, it becomes measure of your loss in that scenario.
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