Page 88 - Handbook Bachelor Degree of Science Academic Session 20212022

Faculty of Science Handbook, Academic Session 2021/2022

Poisson process: exponential distribution, counting
References: processes, distribution of inter-arrival time and waiting time,
1. Elandt-Johnson, R. C., & Johnson, N. L. (1999).
Survival models and data analysis. John Wiley. conditional distribution of the arrival time, nonhomogeneous
2. Benjamin, B., & Pollard, J. H. (1993). The analysis of Poisson process and compound Poisson process.
mortality and other actuarial statistics. Institute and
Faculty of Actuaries. Continuous time Markov chains: birth-and-death process,
3. London, Dick (1998). Survival Models and their transition probabilities and transition rates, limiting
probabilities and time reversibility.
Estimation. ACTEX Publications.
4. Lawless, J. F. (2011). Statistical models and methods
for lifetime data. John Wiley & Sons. Brownian motion and stationary processes: Brownian
5. Macdonald, A. S., Richards, S. J., & Currie, I. D. motion, martingale, hitting time and maximum variable,
maximum of Brownian motion with drift, geometric Brownian
(2018). Modelling mortality with actuarial motion, white noise, Gaussian processes and stationary,
applications. Cambridge University Press.
weakly stationary Processes.
Assessment:
SIQ3011 BUSINESS FINANCE
Continuous Assessment: 40%
Final Examination: 60%
This couse enables the students to understand and deepen
their knowledge of business finance theories. In addition, it
will enable them to understand various advance techniques References:
Ross, S. M. (2014). Introduction to Probability Models
1.
related to risk and return capital structure, dividend policy, (11th Edition), Academic Press.
long-term financing instruments such as bonds and equities, 2.
risk management and mergers and acquisitions. Durrett, R. (2016). Essentials of Stochastic
Processes (3rd Edition), Springer.
3. Serfozo, R. (2009). Basics of Applied Stochastic
Assessment: Processes, Springer.
Continuous Assessment: 40%
Final Examination: 60%
SIT1001 PROBABILITY AND STATISTICS I
References:
1. Berk, DeMarzo & Harford, (2019), Fundamental of Axioms of probability. Counting techniques. Conditional
Corporate Finance, 4th ed. Pearson.
2. Brealey, Myers & Marcus, (2018), Fundamental of probability. Independent events. Bayes Theorem.
Corporate Finance, 9tth ed. McGraw-Hill: New York.
3. Ross, Westerfield & Jaffe, (2016), Corporate Discrete random variables and its mathematical expectation.
Discrete distributions: uniform, hypergeometric, Bernoulli,
Finance, 11th ed. McGraw-Hill. binomial, geometric, negative binomial and Poisson.
4. Brealey & Myers, (2017), Principle of Corporate
Finance, 12th ed. McGraw-Hill: New York.
Continuous random variables and its mathematical
expectation. Continuous distributions: uniform, exponential,
gamma, chi-square and normal.
SIQ3012 FINANCIAL AND BUSINESS MANAGEMENT
Moment generating functions. Distributions of functions of
This course discusses the various financial tools employed one random variable. Independent random variables.
to effectively manage a company’s financial condition and
strategic thinking in financial management. Other topics Distributions of sum of independent random variables.
discussed are financial statement and analysis, time value of Functions related to normal random variables. Central limit
theorem. Approximation for discrete distributions. Limiting
money, bonds and stocks, capital budgeting and its moment generating functions.
techniques and short-term working capital management and
basic legal principles relevant to the work of actuary and
practical implications. Assessment:
Continuous Assessment: 40%
Final Examination: 60%
Assessment:
Continuous Assessment: 40%
Final Examination: 60% References:
1. Hogg, R.V. & Tanis, E.A. (2015). Probability & Statistics
th
Inference, 9 ed., Pearson.
References: 2. Hogg, R.V., McKean, J.W. & Craig A.T. (2019).
1. Gitman, L.J. & Zutter C. J. (2015). Principles of Introduction to Mathematical Statistics, 8 ed.,
th
Managerial Finance, 14th ed. Pearson.
2. Berk, J. and, DeMarzo, P. (2014). Corporate Finance, Pearson.
3rd edition, Pearson. 3. Larson, H.J. (1982). Introduction to Probability Theory
rd
3. Ross. S. A, et. al. (2007). Financial Management & Statistical Inference, 3 ed., Wiley.
Fundamentals in Malaysia. McGraw Hill.
4. Gupta, C.B (2014). Strategic Management, 2nd SIT1002 STATISTICAL PROGRAMMING
Edition.
Introduction to the statistical programming software. Logical
operations. Vector, matrices and arrays. Sequence, decision
SIQ3013 STOCHASTIC MODELS
statement and loops. Writing functions. Data inputs. Data
frames. Graphics. Random number generation. Applications
Introduction to probability theory, conditional probability and
expectation. to statistics.
Markov chains: Chapman–Kolmogorov equations random Assessment:
walk models, classification of states, limiting probabilities, Continuous Assessment: 70%
mean time spent in transient states, branching processes Final Examination: 30%
and time reversible Markov chains.
87