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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)