Principles of Financial Computing. Introduction. Useful Journals. References
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1 Financial Analysts Journal. Useful Journals Journal of Computational Finance. Principles of Financial Computing Prof. Yuh-Dauh Lyuu Dept. Computer Science & Information Engineering and Department of Finance National Taiwan University Journal of Derivatives. Journal of Economic Dynamics & Control. Journal of Finance. Journal of Financial Economics. Journal of Fixed Income. Journal of Futures Markets. Journal of Financial and Quantitative Analysis. Journal of Portfolio Management. Journal of Real Estate Finance and Economics. Management Science. Mathematical Finance. Review of Financial Studies. Review of Derivatives Research. Risk Magazine. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 1 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 3 References Yuh-Dauh Lyuu. Financial Engineering & Computation: Principles, Mathematics, Algorithms. Cambridge University Press Official Web page is Introduction Check for some of the software. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 2 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 4
2 A Very Brief History of Modern Finance 1900: Ph.D. thesis Mathematical Theory of Speculation of Bachelier ( ). 1950s: modern portfolio theory (MPT) of Markowitz. 1960s: the Capital Asset Pricing Model (CAPM) of Treynor, Sharpe, Lintner ( ), and Mossin. 1960s: the efficient markets hypothesis of Samuelson and Fama. 1970s: theory of option pricing of Black ( ) and Scholes. 1970s present: new instruments and pricing methods. What This Course Is About Financial theories in pricing. Mathematical backgrounds. Derivative securities. Pricing models. Efficient algorithms in pricing financial instruments. Research problems. Finding your thesis directions. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 5 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 7 A Very Brief and Biased History of Modern Computers 1930s: theory of Gödel ( ), Turing ( ), and Church ( ). 1940s: first computers (Z3, ENIAC, etc.) and birth of solid-state transistor (Bell Labs). 1950s: Texas Instruments patented integrated circuits; Backus (IBM) invented FORTRAN. 1960s: Internet (ARPA) and mainframes (IBM). 1970s: relational database (Codd) and PCs (Apple). 1980s: IBM PC and Lotus What This Course Is Not About How to program. Basic mathematics in calculus, probability, and algebra. Details of the financial markets. How to be rich. How the market will perform tomorrow. 1990s: Windows 3.1 (Microsoft) and World Wide Web (Berners-Lee). c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 6 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 8
3 Year Municipal Treasury Outstanding U.S. Debts (bln) Mortgage related U.S. corporate Fed agencies Money market Asset backed , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,170.1 Total Computability and Algorithms Algorithms are precise procedures that can be turned into computer programs. Uncomputable problems. Does this program have infinite loops? Is this program bug free? Computable problems. Intractable problems. Tractable problems. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 9 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 11 Complexity Start with a set of basic operations which will be assumed to take one unit of time. The total number of these operations is the total work done by an algorithm (its computational complexity). Analysis of Algorithms The space complexity is the amount of memory space used by an algorithm. Concentrate on the abstract complexity of an algorithm instead of its detailed implementation. Complexity is a good guide to an algorithm s actual running time. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 10 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 12
4 Asymptotics Consider the search algorithm on p. 14. The worst-case complexity is n comparisons (why?). There are operations besides comparison. We care only about the asymptotic growth rate not the exact number of operations. So the complexity of maintaining the loop is subsumed by the complexity of the body of the loop. The complexity is hence O(n). Common Complexities Let n stand for the size of the problem. Number of elements, number of cash flows, etc. Linear time if the complexity is O(n). Quadratic time if the complexity is O(n 2 ). Cubic time if the complexity is O(n 3 ). Exponential time if the complexity is 2 O(n). Superpolynomial if the complexity is less than exponential but higher than any polynomial. It is possible for an exponential-time algorithm to perform well on typical inputs. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 13 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 15 Algorithm for Searching an Element 1: for k = 1, 2, 3,..., n do 2: if x = A k then 3: return k; 4: end if 5: end for 6: return not-found; Growths of Various Functions Time x x 3 x x c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 14 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 16
5 A Common Misconception about Performance A reduction of the running time from 10s to 5s is not as significant as that from 10h to 5h. But this is wrong. What if you have 1,000 securities to price. What if you must meet a certain deadline. Basic Financial Mathematics c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 17 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 19 A Word on Recursion In computer science, it means the way of attacking a problem by solving smaller instances of the same problem. In finance, recursion loosely means iteration. The Time Line Period 1 Period 2 Period 3 Period 4 Time 0 Time 1 Time 2 Time 3 Time 4 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 18 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 20
6 Time Value of Money FV = PV(1 + r) n, PV = FV (1 + r) n. FV (future value); PV (present value); r: interest rate. Common Compounding Methods Annual compounding: m = 1. Semiannual compounding: m = 2. Quarterly compounding: m = 4. Monthly compounding: m = 12. Weekly compounding: m = 52. Daily compounding: m = 365. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 21 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 23 Periodic Compounding If interest is compounded m times per annum, ( FV = PV 1 + m) r nm. (1) Easy Translations An interest rate of r compounded m times a year is equivalent to an interest rate of r/m per 1/m year. If a loan asks for a return of 1% per month, the annual interest rate will be 12% with monthly compounding. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 22 c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 24
7 Example Annual interest rate is 10% compounded twice per annum. Each dollar will grow to be one year from now. [ 1 + (0.1/2) ] 2 = The rate is equivalent to an interest rate of 10.25% compounded once per annum. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 25 Continuous Compounding As m and (1 + r m )m e r in Eq. (1), where e = FV = PVe rn, Continuous compounding is easier to work with. Suppose the annual interest rate is r 1 for n 1 years and r 2 for the following n 2 years. Then the FV of one dollar will be e r 1n 1 +r 2 n 2. c 2004 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 26
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