Statistics for Finance

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Level up your statistics skills for your career in finance with this course in core statistics and finance and business applications. Statistics is the core subject providing the foundation for analysis in all areas of finance. This course, designed and produced by a seasoned financial practitioner, and former math professor, delivers you to the forefront of cutting edge quantitative techniques used in the finance industry worldwide.
This course assumes no knowledge of statistics or finance. From a basic foundation of only high school math this course will elevate you to the forefront of quantitative and computational tools for modelling financial markets, analyzing financial products, and managing risk.
What You Will Learn
This course provides a thorough grounding in the probability foundations of statistics. The core topics of statistics, estimation, hypothesis testing, and confidence intervals, are treated in full depth. Modern statistics methods are applied to real problems from finance.
Some of the topics covered in this course include
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Discrete and combinatorial probability
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The binomial, normal, exponential, and chi-square distributions
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Mixed normal distributions
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Mean, variance, skewness, and kurtosis
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Location, scale, and shape parameters
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Law of large numbers
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Central limit theorem
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Maximum likelihood estimation
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Method of moments
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Hypothesis testing
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Significance level, size, and power of tests
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Confidence bounds and intervals
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Stationarity and structural breaks in financial time series
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Modelling the distributions of financial returns
Includes Python Tools
Python based tools are included for working with probability distributions, for analyzing data, and providing implementations of modern statistical algorithms. All software that is part of this course is released under a permissive MIT license, so students are free to take these tools with them and use them in their future careers, include them in their own projects, whether open source or proprietary, anything you want!
So Sign Up Now!
Accelerate your career by taking this course and advancing your skills in statistics for finance and business. With more than 20 hours of lectures, extensive problem sets, and Python codes implementing modern statistics methods, not to mention a 30 day money back guarantee, you can’t go wrong!
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8Probability PrimerTexto
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9Experiments and Sample SpacesVídeo Aula
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10The Probability FunctionVídeo Aula
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11Further Sample Probability CalculationsVídeo Aula
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12Combinatorial ProbabilityVídeo Aula
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13Conditional ProbabilityVídeo Aula
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14Urn ModelsVídeo Aula
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15Independent EventsVídeo Aula
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16Independence ExamplesVídeo Aula
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17Random VariablesVídeo Aula
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18Cumulative Distribution FunctionsVídeo Aula
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19Discrete Random Variables and Probability DistributionsVídeo Aula
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20Discrete Uniform DistributionVídeo Aula
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21Bernoulli and Binomial DistributionVídeo Aula
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22Sample Binomial CalculationsVídeo Aula
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23Independent Random VariablesVídeo Aula
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24Independent Random Variables: Further ConceptsVídeo Aula
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25Python Distribution LibraryTexto
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26Expected ValueVídeo Aula
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27Expected Values of FunctionsVídeo Aula
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28Linearity of Expected ValueVídeo Aula
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29VarianceVídeo Aula
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30Binomial Distributions: Expected Value and VarianceVídeo Aula
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31Continuous Random VariablesVídeo Aula
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32Expectation and Variance of Continuous Random VariablesVídeo Aula
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33Moments and Moment Generating FunctionsVídeo Aula
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34Discrete Bivariate DistributionsVídeo Aula
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35Marginal DistributionsVídeo Aula
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36Continuous Bivariate DistributionsVídeo Aula
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37Covariance and CorrelationVídeo Aula
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38Multivariate DistributionsVídeo Aula
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39Normal DistributionVídeo Aula
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40Calculations with the Normal DistributionVídeo Aula
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41Sums of Independent Normal Random VariablesVídeo Aula
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42Exponential DistributionVídeo Aula
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43Gamma DistributionVídeo Aula
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44Aspects of the Gamma DistributionVídeo Aula
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45Chi-square DistributionVídeo Aula
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46Location ParameterVídeo Aula
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47Scale ParameterVídeo Aula
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48Location-Scale TransformationsVídeo Aula
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49Location-Scale FamiliesVídeo Aula
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50Transformations of Continuous Random Variables; Change of Variable FormulaVídeo Aula
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51Change of Variable ExamplesVídeo Aula
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52Transformations of Discrete Random VariablesVídeo Aula
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53Quantiles of Continuous Random VariablesVídeo Aula
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54Quantiles of Discrete DistributionsVídeo Aula
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55Location, Scale, and Shape ParametersVídeo Aula
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56Skewness and AsymmetryVídeo Aula
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57Skewed DistributionsVídeo Aula
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58Kurtosis and Fat TailsVídeo Aula
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59Student's t DistributionVídeo Aula
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60Normal Mixture DistributionVídeo Aula
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68Time SeriesVídeo Aula
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69Stochastic Processes and Time Series ModelsVídeo Aula
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70Python Utilities for Statistics for FinanceTexto
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71StationarityVídeo Aula
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72The Marginal Distribution of a Time SeriesVídeo Aula
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73Autoregressive ModelsVídeo Aula
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74Moving Average ModelsVídeo Aula
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75ARMA ModelsVídeo Aula
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76Python Dataset ClassTexto
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77The Sample Moments of Financial Time SeriesVídeo Aula
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78Stationarity vs. Structural BreaksVídeo Aula
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79Market EfficiencyVídeo Aula
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80The Random Walk Model of Asset PricesVídeo Aula
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81The Geometric Random Walk Model of PricesVídeo Aula
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82The Lognormal ModelVídeo Aula
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83Random Walk TestsVídeo Aula
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84Runs TestVídeo Aula
