**In**

Converting One Time Period to Another

Converting One Time Period to Another

*Part 1*, we calculate VAR for the Nasdaq 100 index (ticker: QQQ) and establish that VAR answers a three-part question: "What is the worst loss that I can expect during a specified time period with a certain confidence level?"

Since the time period is a variable, different calculations may specify different time periods - there is no "correct" time period. Commercial banks, for example, typically calculate a daily VAR, asking themselves how much they can lose in a day; pension funds, on the other hand, often calculate a monthly VAR.

To recap briefly, let's look again at our calculations of three VARs in part 1 using three different methods for the same "QQQ" investment:

* We do not need a standard deviation for neither the historical method (because it just re-orders returns lowest-to-highest) or the Monte Carlo simulation (because it produces the final results for us). |

**Applying a VAR Method to a Single Stock**

Both the historical and Monte Carlo simulation methods have their advocates; but the historical method requires crunching historical data, and the Monte Carlo simulation method is complex. The easiest method is variance-covariance.

* Important Note: These worst losses (-19.5% and -27.5%) are losses below the expected or average return. In this case, we keep it simple by assuming the daily expected return is zero. We rounded down, so the worst loss is also the net loss. |

**Conclusion**

Value at risk is a special type of downside risk measure. Rather than produce a single statistic or express absolute certainty, it makes a probabilistic estimate. With a given confidence level, it asks, "What is our maximum expected loss over a specified time period?" There are three methods by which VAR can be calculated: the historical simulation, the variance-covariance method and the Monte Carlo simulation.

The variance-covariance method is easiest because you need to estimate only two factors: average return and standard deviation. However, it assumes returns are well-behaved according to the symmetrical normal curve and that historical patterns will repeat into the future.

The historical simulation improves on the accuracy of the VAR calculation, but requires more computational data; it also assumes that "past is prologue". The Monte Carlo simulation is complex, but has the advantage of allowing users to tailor ideas about future patterns that depart from historical patterns.

To read more on this subject, see

*Continuously Compound Interest*.