been reviewing random variables. RVs have certain properties such as mean that measures the center‚ and variance that measures the dispersion. We would like to make claims about these properties and test them using statistical methods. Over the past years‚ Wall Street has been very interested in the volatility of the stocks. In this case‚ we would want to make sound claims about variances. We start with a null hypothesis Ho‚ which is the claim that we will test. It looks as such: In this
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Econometrics Assignment 2 Group Members : Eliza Tan 01120120073 Praisya Lordrietta 01120120061 Wirhan Pandutama 0112012 UNIVERSITAS PELITA HARAPAN LIPPO KARAWACI-TANGERANG 2014 Gauss-Markov Theorem The Gauss-Markov Theorem is given in the following regression model and assumptions: The regression model (1) Assumptions (A) or Assumptions (B): Assumptions (A) Assumptions (B) E( If we use
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a number of other analysis methods as special cases. We will assume that you are familiar with the basic logic of statistical reasoning as described in Elementary Concepts. Moreover‚ we will also assume that you are familiar with the concepts of variance‚ covariance‚ and correlation; if not‚ we advise that you read the Basic Statistics section at this point. Although it is not absolutely necessary‚ it is highly desirable that you have some background in factor analysis before attempting to use structural
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Normal Distribution Normal distribution is a statistics‚ which have been widely applied of all mathematical concepts‚ among large number of statisticians. Abraham de Moivre‚ an 18th century statistician and consultant to gamblers‚ noticed that as the number of events (N) increased‚ the distribution approached‚ forming a very smooth curve. He insisted that a new discovery of a mathematical expression for this curve could lead to an easier way to find solutions to
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Global Asset Allocation Finance 656 (Please return to Fang Song’s locker #552) Michelle Bien Yushao Karen Chiu Srinivas Mudireddy Fang (Derek) Song‚ 12/08/2013 A Study on stock returns and volatility Abstract This paper applies two models to examine the intertemporal relationship between expected returns and market risk. By using ARIMA models‚ two findings can be found: 1) A positive correlation exists between the expected market risk premium and the predictable volatility. 2)
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probability that it will rain‚ we move from pure simulation to Monte Carlo. The reason for this distinction is that there may be other ways to define A that make it easier to estimate. This process is called variance reduction‚ since most of the error in A is statistical. Reducing the variance of A reduces the statistical error. We often have a choice between Monte Carlo and deterministic methods. For example‚ if X is a one dimensional random variable with probability density f (x)‚ we can estimate
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MEI STRUCTURED MATHEMATICS EXAMINATION FORMULAE AND TABLES 1 Arithmetic series General (kth) term‚ last (nth) term‚ l = Sum to n terms‚ Geometric series General (kth) term‚ Sum to n terms‚ Sum to infinity Infinite series f(x) uk = a + (k – 1)d un = a + (n – l)d – – Sn = 1 n(a + l) = 1 n[2a + (n – 1)d] 2 2 x2 xr = f(0) + xf’(0) + –– f"(0) + ... + –– f (r)(0) + ... 2! r! f(x) f(a + x) uk = a r k–1 a(1 – r n) a(r n – 1) Sn = –––––––– = –––––––– 1–r r–1
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includes a cost variance‚ in which you complete the following: Determine specific strategies to manage budgets within forecasts. Zero based (analyzes every expense within an organization and justifies the need and cost of each)‚ activity based (is the gathering of the operating cost data‚ which is assigned to specifc activies such as engineering) performance based (performance dashboard uses the metrics of performance and analyzes the root cause of financial problems)‚ cost variances (looks at the
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distribution of predictions for the population of financial analysts is normal. a) The probability is 0.10 that the sample variance is more than what percentage of the population variance? b) Determine any pair of numbers a and b to complete the following sentence: The probability is 0.95 that the sample variance is between a% and b% of the population variance. 2. A random sample of 202 vice presidents of marketing in corporations from the manufacturing sector were given a list
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the time series are stationary with respect to the variance and with respect to the mean. First‚ we will assume statistical stationarity of all time series (later on‚ this restriction will be relaxed). Statistical stationarity of a time series implies that the marginal probability distribution is time-independent which means that: bullet the expected values and variances are constant stationary time series - expected values and variances are constant (V.I.1-2) where T is the number
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