Quantifying Market Risk: Thousand Foot View
This is a big picture overview of how financial institutions or hedge funds manage market risk in their portfolios. Financial products can become complex instruments in the derivatives space, and so product valuation is crucial. There are two key essentials that enable financial institutions to offer these products :
(1) Model Price: Modeling the financial product in such a way that you could give it a price at any given time for a set of inputs --> Be able to "mark it to market"
(2) Model Risk Once price is modeled, inputs can be changed in the model to understand how one change in a parameter affects the price of the product --> This is risk!
Risk management, both at the trader level and risk manager seat, are focused on the continuity and survival of the business against statistically unlikely events (and therefore ensuring ongoing cash flow stream) For market risk managers, this involves 3 big focuses:
(1) Timeseries - This data informs the recent market volatility and the Value at Risk model.
(2) Stress Periods - These are sharp directional movements inside of the timeseries data.
(3) Limit Setting - Boundaries created against measurable risk metrics to minimize losses.
Timeseries data is created by the passing of time and at all granularities; Daily, Hourly, Minute and more. Most financial institutions are not trading in and out of positions constantly, and so end of day closing prices become the data series. Day over day change in price creates the "shift". This "shift" is the % change in price. (Side Note: some financial products and derivatives use absolute "shifts" instead of percentage). When combined with the risk of the portfolio, the shifts inform the Value at Risk metric (VAR). There are other vectors besides the 1 day shift (differences day over day), but the 1-Day Value at Risk metric is commonplace and widely used. The other popular shift period length is 10 days.
As the market trades and is open, it is possible for the market to move in a direction that would cause losses for your particular set of holdings/portfolio. This creates a "Stress" on your portfolio from a market risk point of view. (Conversely, market movements that result in portfolio gains also create counterparty risk, due to the possibility you may not get paid your gains - but this is not covered in this discussion). The periods of stress can be quantified, as they are historical records of what happened during a set of events. The "shifts" are now in the timeseries. An example of one of the most recent stress periods is COVID. New "simulated" profit and loss (PnL) results can be estimated each day by combining the current portfolio risk with shifts that occurred over specific, stress time periods.
This last pillar is fairly straightforward and somewhat subjective: Limit setting. Loss appetite is something that is set at the very top of organizations, and then enforced by risk management functions. Limits are created so that the business does not take risk that when combined with a stress scenario or shift results in a loss greater than what was set in the loss appetite. Violations of limits are tracked and reported to management.
So in summary, market risk is essentially combining statistics with the timeseries to set limits around risk taking and estimate simulated losses under different stress scenarios that can inform business decision making. CryptoDataDownload has identified and created a taxonomy of stress scenarios in cryptocurrency markets. Contact us to discuss what our risk services API can do.
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