Market Risk Management: How Banks Manage Market Risks

Market Risk Management of Bank

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.

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.

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

  1. 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.
  2. It is less accurate for untraded assets such as loans, some of which are difficult to price.
  3. 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 * ▲pt
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

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

The Options for VaR Include the Following

  1. 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.
  2. Parametric Method: The method uses a variance-covariance or delta-normal approach. It is a partial valuation model.
  3. 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

  1. 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.
  2. 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.
  3. VaR does not predict bank failure, only losses resulting from a bank s exposure to market risk.
  4. 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.
Subscribe To Our NewsLetter ⁄
Read Related Posts ⁄

Leave a Comment