Franklin D. Roosevelt s Accomplishments - 2007 Words

Franklin D. Roosevelt started his democratic political career in 1910 when he was elected to the New York State Senate due to his aggressive and effective campaign. He then went on to become the Assistant Secretary of the Navy in 1913 when Woodrow Wilson appointed him after he supported Wilson’s campaign (Hamby). While acting as the Assistant Secretary of the Navy, Roosevelt worked to expand the fleet and ordered the preservation of the Navy’s Aviation Division. The position gave FDR an insight to the administrative side of a piece of the U.S. Armed Forces and what was to come as Commander in Chief. After becoming paralyzed from the waist down from polio in 1921, Roosevelt returned to public light in 1928 being elected as the Governor of New York. He worked as a reform governor and created social programs to benefit the state which allowed him to have a strong support base in the most populous state at the time. 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Who Is Nominated Best Speaker - 772 Words

There have been some arguments as to who should be nominated best speaker in History. Indeed, choosing the best speaker in History is not an easy task. We should keep in mind that one of the most important criteria for someone to be the best speaker in History is that their speeches must have a lasting impact on their audience (Mckay). Namely, they must effect change, in thought or deed, in their audience. With this in mind we can see how some try to argue against the fact that this is what Winston Churchill did. One argument people might pose against Churchill, being nominated best speaker in History, is that Churchill was a naturally good speaker and didn’t have to hard as a speaker. First off even if someone was a naturally gifted speaker why should that disqualify them from being named the best speaker in history? If they used that gift to positively impact others, it should not matter. Anyway, Churchill did not have the natural gift of public speaking. 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A Time Series Analysis of the Adjusted Closing Stock Prices Free Essays

Table of Contents 1. ?Introduction 2. ?literature review 3. We will write a custom essay sample on A Time Series Analysis of the Adjusted Closing Stock Prices or any similar topic only for you Order Now ?Introduction 4. ?Methodology INTRODUCTION Google Inc. is an American multinational corporation which provides Internet-related products and services, including Internet search, cloud computing, software and advertising technologies. The company was founded by Larry Page and Sergey Brin while both attended Stanford University. Google was first incorporated as a privately held company on September  4, 1998, and its initial public offering followed on August  19, 2004. The company is now listed on the NASDAQ stock exchange under the ticker symbol . The company’s mission statement from the outset was â€Å"to organize the world’s information and make it universally accessible and useful†, and the company’s unofficial slogan is â€Å"Don’t be evil†. In 2006, the company moved to its current headquarters in Mountain View, California. Objectives 1. To fit a multiple regression model to a data set comprising the put, call and strike prices of a stock belonging to a company listed on a known index. 2. To use the BSM Model to which provides a mathematical science for the pricing and hedging of European Call and Put options as the American Options market 3. We wanted to analyze the data for Google option prices from the S;P index over the past and present time periods in order to be able to forecast the future. Literature Review 1. Put call parity In financial mathematics, put–call parity defines a relationship between the price of a European call option and European put option in a frictionless market —both with the identical strike price and expiry, and the underlying being a liquid asset. In the absence of liquidity, the existence of a forward contract suffices. Put–call parity requires minimal assumptions and thus does not require assumptions such as those of Black–Scholes or other commonly used financial models. 2. Black-Scholes Model The Black–Scholes model or Black–Scholes-Merton is a mathematical model of a financial market containing certain derivative investment instruments. From the model, one can deduce the Black–Scholes formula, which gives the price of European-style options. The formula led to a boom in options trading and legitimized scientifically the activities of the Chicago Board Options Exchange and other options markets around the world. t is widely used by options market participants Methodology The data being analyzed consisted of daily past prices of silver traded on the S;P index since 14th May to 22 September 2012. The group was required to obtain data sets containing put, call and strike prices the data set of option expiring in more than 30 days but less than 100 The data was obtai ned from marketwatch. com on 14th May 2012 copied to excel and imported to R, with the stock price at $605. 23. The group chose options expiring on 22th September 2012 for the 1st data set, with 94 days to expiry. An average of the Bid and Ask prices of both the call and put options was then calculated as shown below. The values in the columns labeled â€Å"call†; â€Å"put â€Å"were calculated as an average of the corresponding Bid ; Ask call and put prices respectively. A number of statistical methods were applied to analyze the data on the R program. We first started by importing the data to the R program; below is a table showing the data. Strike | call| put| Strikesq| Adj Close| 295| 311. 75| 0. 45| 87025| 605. 23| 300| 306. 45| 0. 425| 90000| 613. 66| 305| 303. 1| 0. 45| 93025| 609. 15| 310| 297. 6| 0. 75| 96100| 612. 79| 315| 291. 4| 0. 5| 99225| 607. 55| 320| 286. 6| 0. 55| 102400| 596. 97| 325| 282. 75| 0. 6| 105625| 611. 02| 330| 277. 85| 0. 65| 108900| 607. 26| 335| 273. 4| 0. 775| 112225| 604. 43| 340| 266. 6| 0. 7| 115600| 604. 85| 345| 262. 1| 0. 75| 119025| 614. 98| 350| 256. 7| 0. 8| 122500| 615. 47| 355| 253. 3| 0. 875| 126025| 609. 72| 360| 248. 35| 0. 875| 129600| 601. 27| 365| 243. 45| 0. 925| 133225| 597. 6| 370| 237. 35| 1| 136900| 596. 06| 375| 232. 4| 1. 05| 140625| 599. 3| 380| 227. 45| 1. 05| 144400| 607. 45| 385| 222. 55| 1. 2| 148225| 609. 57| 390| 218. 85| 1. 325| 152100| 606. 7| 395| 212. 45| 1. 45| 156025| 624. 6| 400| 207. 9| 1. 525| 160000| 651. 01| 405| 202. 95| 1. 6| 164025| 635. 96| 410| 198. 15| 1. 65| 168100| 626. 86| 415| 193. 15| 1. 825| 172225| 630. 84| 420| 188. 4| 2. 025| 176400| 632. 32| 425| 183. 6| 2. 25| 180625| 635. 15| 430| 180| 2. 375| 184900| 642. 62| 435| 175. 25| 2. 55| 189225| 646. 92| 440| 170. 3| 2. 9| 193600| 641. 24| 445| 164. 6| 3. 025| 198025| 648. 41| 450| 160. 9| 3. 3| 202500| 655. 76| 455| 155. 15| 3. 55| 207025| 647. 02| 460| 150. 6| 3. 85| 211600| 649. 33| 465| 146. 8| 4. 05| 216225| 642. 59| 470| 141. 15| 4. 55| 220900| 646. 05| 75| 137. 65| 4. 95| 225625| 639. 98| 480| 132. 05| 5. 35| 230400| 633. 49| 485| 128. 5| 5. 8| 235225| 633. 98| 490| 123. 45| 6. 2| 240100| 625. 04| 495| 118. 65| 6. 75| 2 45025| 621. 13| 500| 114. 1| 7. 4| 250000| 615. 99| 505| 110. 75| 7. 95| 255025| 617. 78| 510| 105. 65| 8. 5| 260100| 605. 15| 515| 101. 35| 9. 45| 265225| 600. 25| 520| 98| 10. 25| 270400| 607. 14| 525| 93. 15| 11. 1| 275625| 606. 8| 530| 89. 55| 11. 95| 280900| 604. 96| 535| 85. 15| 13. 05| 286225| 614. 25| 540| 80. 6| 14. 15| 291600| 621. 25| 545| 76. 85| 15. 3| 297025| 622. 4| 550| 72. 9| 16. 35| 302500| 618. 25| 555| 69. 5| 17. | 308025| 618. 39| 560| 66. 05| 19. 2| 313600| 609. 31| 565| 62. 8| 20. 75| 319225| 609. 9| 570| 59. 15| 22. 45| 324900| 606. 11| 575| 56. 5| 24. 05| 330625| 607. 94| 580| 52. 75| 26| 336400| 614| 585| 49. 7| 27. 85| 342225| 604. 64| 590| 46. 7| 29. 6| 348100| 606. 52| 595| 43. 9| 31. 9| 354025| 605. 56| 600| 40. 95| 34. 35| 360000| 609. 76| 605| 38. 45| 36. 7| 366025| 612. 2| 610| 36. 1| 38. 85| 372100| 605. 91| 615| 33. 55| 41. 35| 378225| 611. 46| 620| 31. 05| 44. 15| 384400| 609. 85| 625| 29. 5| 46. 9| 390625| 606. 77| 630| 27. 35| 49. 75| 396900| 60 9. 09| 635| 25. 3| 52. 95| 403225| 596. 33| 40| 23. 2| 56| 409600| 585. 11| 645| 21. 6| 59. 2| 416025| 580. 83| 650| 19. 95| 62. 65| 422500| 580. 11| 655| 18. 6| 66. 15| 429025| 577. 69| 660| 16. 85| 70. 1| 435600| 579. 98| 665| 15. 6| 73. 95| 442225| 568. 1| 670| 14. 4| 77. 55| 448900| 569. 49| 675| 13. 3| 81. 35| 455625| 580. 93| 680| 12. 25| 85. 55| 462400| 585. 52| 685| 11. 05| 88. 25| 469225| 585. 99| 690| 10. 05| 93. 4| 476100| 639. 57| 695| 9. 55| 96. 45| 483025| 632. 91| 700| 8. 45| 102. 25| 490000| 628. 58| 705| 7. 75| 105. 25| 497025| 624. 99| 710| 7. 1| 110. 65| 504100| 629. 64| 715| 6. 75| 114. 75| 511225| 625. 96| 720| 5. 95| 119. 5| 518400| 623. 14| 725| 5. 65| 122. 65| 525625| 622. 46| 730| 5. 05| 128. 5| 532900| 650. 02| 735| 4. 55| 131. 95| 540225| 659. 01| 740| 4. 25| 137. 6| 547600| 668. 28| 745| 3. 95| 142. 35| 555025| 665. 41| 750| 3. 5| 147| 562500| 645. 9| 755| 3. 25| 151. 7| 570025| 642. 4| 760| 2. 975| 155. 95| 577600| 639. 7| 765| 2. 725| 161. 4| 585225| 64 0. 25| 770| 2. 525| 166. 45| 592900| 633. 14| 775| 2. 2| 169. 9| 600625| 629. 7| 780| 2. 125| 174. 75| 608400| 625. 82| 785| 1. 975| 180. 55| 616225| 630. 37| 790| 1. 775| 185. 45| 624100| 621. 83| 795| 1. 65| 190. 35| 632025| 625. 96| 800| 1. 525| 195. 15| 640000| 619. 4| 810| 1. 35| 205. 05| 656100| 618. 07| 820| 1. 175| 214. 95| 672400| 625. 63| 830| 0. 975| 224. 75| 688900| 625. 39| 840| 0. 825| 234. 95| 705600| 627. 42| 850| 0. 725| 244| 722500| 616. 05| 860| 0. 65| 254. 25| 739600| 623. 39| 870| 0. 525| 265| 756900| 623. 77| 880| 0. 475| 274. 55| 774400| 625. 65| 890| 0. 425| 284. 6| 792100| 620. 36| 900| 0. 375| 293. 45| 810000| 613. 77| 910| 0. 375| 304. 7| 828100| 599. 39| 920| 0. 3| 314. 45| 846400| 582. 93| 930| 0. 3| 323. 3| 864900| 588. 19| 940| 0. 275| 333. 25| 883600| 563| 950| 0. 25| 343. 25| 902500| 570. 11| 960| 0. 25| 353. 25| 921600| 580| 970| 0. 25| 363. 25| 940900| 580. 94| Fitting a Multiple regression Model From the results shown? 0=605. 997, ? 1=0. 995, ? 2= -0. 9979. The value of the stock at that point in time wasSt=605. 23. If significant, the estimate ? 2 was to be equated to -e-r (T-t) and the value for r equated. In this formula, T-t is the time to expiry of the options (94 days in our case) and r is the interest on a daily basis (short rate), which was then supposed to be annualized. Since all the estimates were significant, ? 2= -0. 9979=-e-r(94) r=-ln0. 997994=2. 236391*10-5 Annualizing r; r=2. 236391*10-5*250=0. 05592275=5. 2275%, which is the risk. The formula call(Ct)= 605. 997+0. 995put(Pt)-0. 9979(Strike(Kt)) was the model we used to derive values of call prices in relation to the multiple regression model. A plot of these call and strike options is shown below; If significant, the estimate ? 2 was to be equated to -e-r (T-t) and the value for r equated. In this formula, T-t is the time to expiry of the options (94 days in our case) and r is the interest on a daily basis (short rate), which was then supposed to be annuali zed. PROCEDURE FOR FITTING Finally we drew a graph of Call against Strike and this was the graph obtained. The code and resulting graph are shown below, GRAPH FOR CALL AGAINST STRIKE BSM MODEL METHODS To fit the BSM Model and generate theoretical call prices, we obtained and truncated historical data from finance. yahoo. com as shown in the column labeled ‘Adj. Close’ The code snapshot below created a function â€Å"BSM73† We then computed the BSM73 by using the given the data, annualized interest rate (r), stock price, strike price and days to maturity generates the theoretical call prices. The proposed model to be fitted to fit the regression model CtSt= ? 0+? 1KSt+? 2K2St+ ? t main purpose is so as to determine the values of ? ,? 1 ; ? 2 Procedure From the results shown, we get ? 0=1. 313950 , ? 1=-1. 959886, ? 2= 0. 001195. The value of the stock at that point in time was St=605. 23. PLOT BSM CALL PRICE (Yt) AGAINST STRIKE PRICES For data analysis conducted for September 2012 options with T-t=94 days and r=5. 922%, the proposed model can be used in option pricing. It can be concluded from the analysis that for options with a longer time to expiry and a smaller interest rate, the proposed model prices the options more accurately than the BSM model in the price ranges where most options are traded. TIME SERIES ANALYSIS The theoretical model for a time financial time series data is given by; Xt = Trend + ARMA + GARCH + WN Where WN is the white noise in the data. We assumed that the GARCH component is equal to 0 We proceeded to investigate whether indeed the data at hand had trend in it. We used the following tools in our investigation * Box plots * ACF * Histogram * Plotting the data Time series of the data. Summary of strike price data. Box plots ACF OF GARCH NOISE Code: ;y=log(strike) ; d=diff(y) ; garch=d^2 ; acf(garch,lag=100,main=†ACF of Garch Noise†) Histogram Code: hist(strike,main=†Histogram of ADJ Closing prices†) De-trending the data After having confirmed that the data contained linear trend, we proceeded to de-trend the data by; 1. Finding the natural logarithm of the data ;y=log(strike) 2. Differencing the data ;d=diff(y) We confirmed that the data the data was actually stationery at this point by using the following techniques; * Finding the ACF of the de-trended data and Plotting the de-trended data FIT ARMA (p, q) We found that an ARIMA (2, 2, 0) was the best model for our data FORECASTING We used the ARIMA (2,2,0) model to predict the adjusted closing share prices for the next 10 days: How to cite A Time Series Analysis of the Adjusted Closing Stock Prices, Essay examples

Bipolar Disorder Cause of Great Madness or Great G Essay Example For Students

Bipolar Disorder Cause of Great Madness or Great G Essay enius Essay written by Is bipolar disorder the cause of great madness or great genius? The symptoms of this mental illness may also be considered as the driving forces behind some of the most gifted and talented people to grace our society. Although individuals with this illness may have some obstacles to overcome, it can be accomplished. With all of the treatment programs that are widely available, people have many options and methods to turn to for help. Bipolar disorder, also referred to as manic depression, is a mood disorder. A person with bipolar disorder will have extreme mood shifts between mania, a state of highly elevated euphoric feelings, and depression, a state of despondency and despair. These shifts can take weeks, days, or even minutes to happen. The period between shifts will vary for each individual, depending on the severity of the disorder (Williams Wilkins, 1999, pp. 5-35). Approximately two percent of adults have this mental illness, and about fifteen percent of those adults will attempt suicide. Bipolar disorder affects both men and women and the affected rates are similar between different cultures and countries. Most people with bipolar disorder experience their first mood episode in their twenties, although it is not uncommon to experience the first episode during childhood or in late life (Bi-polar Disorder: Innovative Research in Health, 2000). Bipolar disorder is not a curable disorder, although it is treatable. Bipolar is among the most treatable of the psychiatric illnesses (Manic-Depressive/Bipolar Disorder, 2000). It is important for people who believe that they may need help to seek it as soon as possible because the earlier that bipolar disorder can be diagnosed the earlier treatments can start. Even after experiencing an episode, even after sensing that something may be wrong, individuals who seek help may not initially receive the correct diagnosis from a medical professional. Because of the similarity of symptoms, bipolar disorder may initially be misdiagnosed as panic disorder, schizophrenia, or attention deficit disorder (Bi-polar Disorder: Innovative Research in Health, 2000). Bipolar may also be difficult to diagnose because the person seeking help may not be telling the doctor everything the doctor needs to know to correctly identify the problem. Medical professionals may only diagnose a person as having depression because they have no knowledge of the excessive enthusiasm that the patient feels. It is important for individuals seeking medical help to be accurate and thorough in describing their feelings or symptoms (Manic-Depressive/Bipolar Disorder, 2000). A person experiencing a manic episode may have increased energy and/or racing thoughts. Feelings of euphoria and/or an increased pressure to talk may also be symptoms. A person in this stage of the illness may have uncharacteristically poor judgement and/or may be involved in some type of high-risk behavior, such as uncontrollable spending sprees, habitual reckless driving and/or participating in unusual sexual encounters and behaviors. Usually the individual denies that anything is wrong when in a manic state (Manic-Depressive/Bipolar Disorder, 2000). A person experiencing the depression state of bipolar disorder may have feelings of worthlessness and/or guilt. Decreased energy and/or loss of interest in once pleasurable activities may also be symptoms of clinical depression. A depressed person may have trouble concentrating and/or have trouble making decisions. Depression may also leave individuals contemplating suicide (Manic-Depressive/Bipolar Disorder, 2000). There are two classifications for bipolar disorder, bipolar II and bipolar I (Williams Wilkins, 1999, pp. 5-35). The combination and degree of mania and depression determine the type of bipolar illness. It is also determined by how long each stage lasts and the time frame of euthymia, having normal moods, between stages. The cycling of stages may overlap, which is referred to as a mixed episode. The diagnostic system that is currently being used by mental health professionals is the Diagnostic and Statistical Manual of Mental Disorders volume four, also known as DSM-IV (Manic- Depressive/Bipolar Disorder, 2000). A patient diagnosed as having bipolar II disorder has or has had at least one episode of major depression and is experiencing or has experienced one or more episodes of hypomania. 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