# Market Risk Management: How Banks Manage Market Risks

The central components of a market risk management system are RAROC (risk-adjusted return on capital) and value at risk (VaR). RAROC is used to manage risk related to different business units within a bank and evaluate performance.

**Table of Contents**

## Time Horizon for Measuring Risk Exposure

Risk measurement is based on time horizon; ideally, risk measurement would be based on a 5 or 10 years time horizon.

- Investment banks have higher confidence intervals but a short holding period because they can quickly unwind their positions.
- But traditional banks cannot unwind their positions quickly. That’s why they set a lower confidence interval and have a longer time horizon than investment banks.

## Probability Distribution of Potential Outcomes

The probability distribution of potential outcomes is necessary to measure market risk, such as the probability of default or loss on a portfolio. The traded asset prices are usually assumed to follow the normal distribution, but sometimes the distribution may be skewed.

## Limitations of RAROC

- The risk factor for each category is assigned according to the historical volatility of its market price. Still, there is no guarantee that the past is a good predictor of the present or future.
- It is less accurate for untraded assets such as loans, some of which are difficult to price.
- It is difficult to choose a single hurdle rate or benchmark.

## Market Risk and Value at Risk

- The VaR model measures a bank’s market risk and serves a different purpose from RAROC.
- Though VaR was originally used as an internal measure by banks, it assumed even greater importance for some years. The distinguishing feature of a VaR is the emphasis on losses arising from the volatility of assets instead of the volatility of earnings. JP Morgan developed the 1st comprehensive model, and the formula is

VaRx = Vx * Dv / Dp * ▲ptWhere; Vx; the market value of portfolio x.Dv/Dp; the sensitivity to price movement per dollar market value. ▲pt; the adverse price movement of t maybe a day, a month, etc. |

Value at risk estimates the likely or expected maximum amount dial could last on a bank’s portfolio due to a change in risk factors, i.e., the price of underlying assets over a specific time horizon within a statistical confidence interval.

## Assumptions for Computing VaR

- The frequency of computation, daily, monthly, quarterly, etc.
- Identification of the position or portfolio affected by market risk.
- The risk factors affecting the market position.
- The confidence interval – 99% and one-tailed.
- The holding period – depends on the objective of the exercise.
- Choice of the frequency distribution.

## The Options for VaR Include the Following

**Non- parametric Method**: This method uses historical simulations of past risk factors returns but makes no assumption on how they are distributed. It is known as a full valuation model.**Parametric Method**: The method uses a variance-covariance or delta-normal approach. It is a partial valuation model.**Monte Carlo Approach**: Another full valuation approach that involves multiple simulations.

## VaR, Portfolios, and Markel Risk

The components of any portfolio are sensitive to certain fundamental risks. These are as follows:

**Delta or absolute price risk**: The risk that the underlying asset’s price will change.**Vega or Volatility Risk**: The risk dial when an option is involved or a product has characteristics similar to an option.**Rho or Discount Risk**: This risk applies primarily to derivatives or products valued using a discount rate.**Theta or Time Decay Risk**: The time value of the option. A change in the portfolio’s value because of the passage of time.

**Problems with the VaR Approach**

- It does not give the precise amount that will be lost statistically. Rather than giving the entire tail, it is giving an arbitrary point in the tail.
- The simpler VaR models depend on the assumption that financial returns are normally distributed, and non-correlated financial studies have shown that these assumptions may not hold, contributing to an inaccurate VaR measure of market risk.
- VaR does not predict bank failure, only losses resulting from a bank s exposure to market risk.
- The parametric and non-parametric frequency distribution produce measures relying on historical data. An implicit assumption is that they are good predictors of future returns. Still, historical simulation is sensitive to the sampling period.