Market Risk Management in Fund Management

Several individuals have a higher appetite for risk and seek higher returns. Typically they entrust their funds with one kind of fund or the other. The funds that have lesser controls and are considered more risky are called hedge funds. These funds contrary to their name are considered more risky than most. The managers of these funds therefore need to be adept at identifying, assessing and managing these risks. The fund managers themselves will not be hit by market risk losses, since they are managing the funds for their clients. Each fund manager reputation stands at stake based on their fund’s performance.

Fund managers need to generate returns by taking a certain amount of risks. They need to disclose the kind of risks they are taking to their investors right at the outset. Clients need to agree to their conditions and the managers need to adhere to the terms of agreement.

Identification         

It is important that fund managers not overlook any risk. For instance, in the case of hedge funds secondary risks must not be overlooked. Fund managers need to identify risks on both the asset side and the liability side. Previously accounting standards and actuarial practices were used to mask these risks to a certain degree; new rules however require managers to clearly describe all risks.

Assessment

The assessment of risk can be ex-ante or ex-post. Ideally ex-ante analysis should be done by fund managers. Ex-post can at best be used to assess performance against forecasts. Ex-post is a pure statistical analysis. This is usually done for the time series of returns, which produces estimates for returns distributions. These estimates are run by Risk Adjusted Performance Measures or RAPMs like the Sharpe Ratio.

Another method of assessment is to compare a fund against similar funds. A benchmark is set by comparing funds and a particular fund is compared against this benchmark. The tracking error, the standard deviation of returns to benchmark is arrived at. Using this, the information ratio or appraisal ratio is arrived at. This is the ratio of average return relative to the benchmark over the tracking error. Fund managers while conducting a performance analysis, include an attribute analysis as well to justify their strategies and therefore the profits or losses that the fund has made.

Ex-post analysis is essentially like looking at the rear view mirror. Typically a short trail of historical data of a few years is used. It is important that an ex-ante analysis be conducted. To assess long term risk, measures such as exponential moving averages prove to be inadequate, despite being able to forecast into the near future.

This requires that managers assess and forecast market conditions. This brings in the factor of subjectivity or instinct. Criteria for trading, limits and contingency plans must be assessed thoroughly for a good ex-ante analysis.

If the trader assumes that future deviations from the benchmark will be small, since they have been so in the past, that is tracking error will be small, it would be erroneous. Tracking errors in ex-post analysis are done using de-trended return series (The data is detrended by removing the data related to a trend so that the real factors for changes can be identified.). Therefore the criteria being used for one portfolio will not fit all. It is important that assessment factors best suited for the objective and returns of the fund under consideration be used to estimate future risk.

Mitigation

The control and mitigation of market risk in funding is considered as complex as the formulation of the investment strategy of the fund itself. Some of the methods adopted are selective hedging, momentary hedging and capital protection to name a few. (You can read Mitigation of Market Risk in Fund Management).

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.