## Test your basic knowledge |

# FRM Foundations Of Risk Management Quantitative Methods

**Instructions:**

- Answer 50 questions in 15 minutes.
- If you are not ready to take this test, you can study here.
- Match each statement with the correct term.
- Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.

**1. Variance of weighted scheme**

**2. BLUE**

**3. Weibul distribution**

**4. R^2**

**5. Variance(discrete)**

**6. Priori (classical) probability**

**7. LAD**

**8. EWMA**

**9. ESS**

**10. Antithetic variable technique**

**11. Significance =1**

**12. What does the OLS minimize?**

**13. Adjusted R^2**

**14. GPD**

**15. LFHS**

**16. Statistical (or empirical) model**

**17. Beta distribution**

**18. Standard error**

**19. Pooled data**

**20. Tractable**

**21. Hazard rate of exponentially distributed random variable**

**22. Continuously compounded return equation**

**23. Perfect multicollinearity**

**24. Continuous random variable**

**25. Lognormal**

**26. Two drawbacks of moving average series**

**27. Kurtosis**

**28. Variance of X+Y assuming dependence**

**29. Mean reversion**

**30. Poisson distribution equations for mean variance and std deviation**

**31. P - value**

**32. Sample correlation**

**33. Exponential distribution**

**34. Potential reasons for fat tails in return distributions**

**35. SER**

**36. Multivariate probability**

**37. Deterministic Simulation**

**38. Unconditional vs conditional distributions**

**39. Confidence ellipse**

**40. Heteroskedastic**

**41. Test for unbiasedness**

**42. Homoskedastic only F - stat**

**43. Covariance calculations using weight sums (lambda)**

**44. Importance sampling technique**

**45. Central Limit Theorem(CLT)**

**46. Implications of homoscedasticity**

**47. Variance of sampling distribution of means when n<N**

**48. Limitations of R^2 (what an increase doesn't necessarily imply)**

**49. Homoskedastic**

**50. Least squares estimator(m)**