Actuarial techniques master's program (TACT) is organized in partnership by ASE Bucharest and the Institute of Financial Studies - a partnership that provides a combination of didactic and practical for those who are interested in working in the actuarial techniques. Special guests are actuaries and / or risk managers with experience in the Romanian market and the international market. The program benefits from the participation of lecturers as members of the Romanian Association of Actuaries.
The program is modular, there are 5 modules in each semester of the fourth, each of them being completed with a written evaluation. The program has the support of insurance companies regarding the internships for students in the second year of the master and job offers beneficial for participants who do not work in this field.
TACT was founded in 2005 and was organized 13 series till now, where have participated a number of 433 people.
The benefits of TACT are:
For more details, please visit the official program of master TACT.ASE.RO
PhD. CRISTESCU Amalia Florina
1. Economic theories and models. Analysis of recent economic developments.
2. Consumers behaviour analysis. Economic utility. Indifference curves. Demand. Elasticity of demand.
3.Firms behaviour analysis. Production function. Production costs. Profit. Supply. Elasticity of supply.
4. Markets analysis. Perfect competition vs. imperfect competition. Profit maximization.
5. Analysis of the relationships between economic policies, markets and firms. Globalization and multinational economic activities. Macroeconomic models on functional blocks.
6. Macroeconomic balance and propagation of macroeconomic shocks. Macroeconomic Policies' Efficiency. Fiscal-budgetary policy. Monetary policy. The mix of economic policies. Influences on the insurance market.
7. Balance of payments. Exchange Rate Management. Importance of international trade. The IS-LM-BP model
PhD. NAGHI Laura Elly
PhD. MIRCEA Iulian
1. Introductory Course: Course objectives and skills acquired as a result of learning are presented; the methods, working tools are specified, as well as the formative assessment requirements and standards throughout the study and the final evaluation.
Theory of interest rates: cashflow models, time value of money, discount, instantaneous rate, present value, real interest rate and monetary interest rate (money interest rate), accumulation, simple interest, compound interest, equivalent capitals, discounting operations.
2. Equation of value and its applications: examples of equations with certain payments, and their use in reimbursement of loans, calculation of price or running and redemption yield of a bond.
3. Shares valuation, calculation of the share yield. Project valuation, calculation of the net present value, calculate the internal rate of return. Efficient markets hypothesis, rational choice theory, utility functions, comparing investment opportunities.
4. Properties of risk measures. Variance of return, Value at Risk (VaR), Tail VaR. Forward contracts, forward rates, forward price, forward risk-neutral measure. Uncertainty approach (assessment). Fuzzy sets.
5. Modeling interest rate dynamics; stochastic models for investment returns, stochastic interest rate model.
6. Option valuation and arbitrage; lower and upper bound of an option price; European and American option valuation; Binomial model and the Black-Scholes model.
7. Financial derivatives; Zero-coupon bonds; Evaluation of call options issued on zero-coupon bonds; Financial asset management.
PhD. IFTIMIE Bogdan
1. Elementary Probability. Random variables, cumulative distribution function. Discrete random variables. Classical discrete distributions: Binomial, Poisson, Negative Binomial and Geometric.
2. Continuous random variables, probability density function, Classical continuous distributions: Normal, Lognormal, Gamma, Exponential, Chi Square, Student, Uniform, Pareto.
3. Raw and central moments of r.v. Expectation. median, mode, variance, skewness and kurtosis. Properties. Quantiles of distributions.
4. Moment generating function for r.v. Conditional expectations. Excess loss variable and mean excess loss function. Left-censored and shifted r.v. Limited loss r.v. Applications to insurance.
5. Raw and central moments of random vectors. The covariance and correlation coefficient for two random variables. Marginal distributions. Distribution of the sum of two independent random variables determined as the convolution of their probability density functions.
6. Law of large numbers. Central limit theorem. Applications for large insurance/finance portfolios for which losses are modeled via i.i.d. random variables.
7. Loss random variables associated to insurance contracts with coverage modifications: with/without franchise deductibles, with policy limits, reinsurance. Loss elimination ratio. Determination of cdf of loss, survival function, expected loss, variance of loss.
8. (a, b, 0) class. Compound frequency models. Panjer recursive formula for (a, b, 0) class. Aggregate loss models.
9. Copulas. Characterization of cumulative distribution function of a random vector by marginal cdf and copula function. Sklar’s theorem. Measures of tail dependence: Spearman’s rho and Kendall’s tau. Gaussian copulas. Archimedian copulas.
10. Introduction in Extreme Value Theory. Extreme Value Distributions: Gumbel, Frechet, Weibull. Fisher-Tippett theorem.Block Maxima method. Generalized Pareto distributions. Balkema-de Haan-Pickands theorem.
11. Random samplings and statistical inference. Random samples from a general statistical population. Sample mean and sample variance. Asymptotic distribution of sample mean. t-statistic for random samples from a normal population. F distribution for ratio of sample variances from
two independent statistical populations.
12. Estimators for parameters of statistical populations. Properties. Methods for point estimates: method of moments and method of maximum likelihood. Asymptotic properties for estimators obtained by maximum likelihood estimation method.
13. Confidence intervals for parameters of statistical populations. Confidence intervals for the mean and variance of a normal population. Confidence intervals for the parameter p of Binomial distribution. Confidence intervals for the difference of the means of two independent statistical populations.
14. Hypothesis testing and goodness of fit. Null and alternative hypotheses, type I and type II errors, critical region, power of a test, p-value. Hypothesis testing for normal, Binomial and Poisson populations. Chi-Square and Kolmogorov-Smirnov goodness of fit tests.
PhD. COVRIG Mihaela
1.Presentation of suitable exploratory methods in data analysis of univariate and bivariate data:
2.Presentation of the Principal Component Analysis as a method of dimentionality reduction.
3.Simple linear regression model:
4.Multiple linear regression model:
5.Generalised linear models:
6.Estimation methods of lifetime distributions:
7.Graduation: techniques and tests; mortality table application.
Prof. FULGA Cristinca
1.Generating Random Numbers and Random Variables
2.Foundations of Monte Carlo Simulations
3.Statistical Analysis of Simulated Data. Statistical Validation Techniques
4.Simulation of Discrete-Events Systems
5.Simulating Financial and Actuarial Models
PhD. SOLOMON Ovidiu
1.Backwards shift operator, backwards difference operator and the roots of the characteristic equation of time series. Linear filter.
2.Linear stationary models for the time series analysis.
3.Moving average (MA) models.
4.Linear stationary models for the time series analysis.
5.Autoregressive (AR) and autoregressive moving average (ARMA) models.
6.Deterministic forecasts from time series data using simple extrapolation and moving average models, applying smoothing techniques and seasonal adjustment when appropriate. Applications of a time series model to economic variables.
7.Criteria for choosing between models and diagnostic tests applicable to the residuals of a time series.
8.Nonstationary models for time series.
9.Autoregressive integrated moving average (ARIMA) models. The Box-Jenkins methodology.
10.Cointegrated time series. Engle-Granger methodology.
11.Multivariate autoregressive model. Non-linear models for non-stationary time series.
12.Discrete random walks and random walks with normally distributed increments, with and without drift.
13.Univariate time series with Markov property.
1. Portfolio management principles
2. Actuarial mathematics in non-life insurance
3. Actuarial mathematics in life insurance
4. Stochastic processes
5. Actuarial reports
1. Quantitative risk management
2. Scientific seminar3. Internship
Admission session 2018-2020
Admission takes place in ASE Bucharest only between 23-25 of July (08.00 a.m.-16.00 p.m).
Admission is based on a written test in the form of a grid test. The score on the basis of which candidates will be assessed will be obtained from the results of the grid test and the average of the Bachelor's Degree Completion Exam, calculated according to the annual ESA Admission Methodology.
Director - Prof. Laura Elly NAGHI, mailto: firstname.lastname@example.org
Deputy Director - Prof. Bogdan IFTIMIE, mailto: email@example.com
Mariana Eftimie, mailto: firstname.lastname@example.org
Emilia ROBU, mailto: email@example.com
Address: Bucharest Academy of Economic Studies
Faculty of Finance and Banking
5-7 Mihail Moxa Street, , District 1,010961, Bucharest
Tel: +4021.319.19.01 extension 565
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