Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business

FRM Track Courses

Financial Risk Management (FRM) Track Courses

(MS-FIN Program)

Starting 2018/2019

This specialized sequence of courses in the MS-FIN Program supports students seeking to prepare for the Financial Risk Manager (FRM) designation exams administered by the Global Association of Risk Professional (GARP).  The subject matter, skills, modes of analysis, and techniques tested on the GARP FRM exams include foundational material from the economics, probability and statistics, and finance disciplines.  This sequence of courses covers and draws on the fundamental tools from these disciplines and develops the set of approaches and techniques employed by professional financial risk managers to analyze, identify, quantify, and manage financial risk, via minimization, mitigation, or risk transfer. 

 

Required Courses:

BANA 7031: Probability Models (4 credit hours)

BANA 7041: Statistical Models (4 credit hours)

BANA 7050: Forecasting and Time Series Methods (2 credit hours)

FIN 7020: Theory of Financial Decision Making (3 credit hours)

FIN 7037: Fixed Income (3 credit hours)

FIN 7042: Options and Futures (3 credit hours)

FIN 7045: Portfolio Management (3 credit hours)

FIN 7052: Securities Trading and Markets (2 credit hours)

FIN 7054: Risk Management (3 credit hours)  

IRM 7060: Advanced Financial Risk Management (3 credit hours)

IRM 7065: Applied Projects in Financial Risk Management (3 credit hours)

ACCT 8089: Financial Statement Analysis (2 credit hours)  

 

FRM-support Electives:

BANA 6025: Optimization Models (2 credit hours)

BANA 7035: Simulation Analysis (2 credit hours)

BANA 7042: Statistical Modeling (2 credit hours)

BANA 7046: Data Mining I (2 credit hours)

BANA 7047: Data Mining II (2 credit hours)

BANA 6037: Data Visualization (2 credit hours)

FIN 7072: Behavioral Finance (2 credit hours)

FIN 7046: Alternative Investments (2 credit hours)

FIN 7055: International Finance (2 credit hours)

STAT 6042: Survival Analysis and Logistic Regression (3 credit hours)

STAT 6045: Statistical Computing with SAS and S-plus (3 credit hours)

 

Required Courses:

BANA 7031: Probability Models (4 credit hours)

PROBABILITY MODELS: Events, probability spaces and probability functions; Random variables; Distribution and density functions; Joint distributions; Moments of random variables; Special expectations; Moment generating functions; Conditional probability and conditional moments; Probability inequalities; Independence; Special probability distributions including: binomial, negative binomial, multinomial, Poisson, gamma, chi-square, normal, beta, t, F, mixture distributions, multivariate normal; Distribution of functions of random variables; Order statistics; Asymptotic results including: convergence in distribution, central limit theorem, convergence in probability, Slutsky's theorem STOCHASTIC MODELS: Discrete time Markov processes, Markov pure jump processes, Birth and death processes, Branching processes, Poisson process, Pure birth processes, Yule process; applications in several areas, e.g. queuing models, machine repair models, inventory models, etc.

BANA 7041: Statistical Models (4 credit hours)

Basic estimation, hypothesis testing, and data analysis. Point and interval estimation. One factor ANOVA. Fitting and drawing inferences from simple and multiple linear regression models. Variable selection procedures. Residual diagnostics and model correction procedure for linear regression.

BANA 7050: Forecasting and Time Series Methods (2 credit hours)

This is a course in the analysis of time series data with emphasis on appropriate choice of forecasting, estimation, and testing methods. The course covers univariate Box-Jenkins methodology for fitting and forecasting time series; ARIMA models; stationarity; non-stationarity; auto-correlation functions; partial and inverse autocorrelation functions.  Estimation and model fitting.  Diagnosing time series models.  Forecasting: point and interval forecasts, seasonal time series models; transfer function models; intervention models; modeling volatility with ARCH, GARCH, and other methods; modeling time series with trends.  Multiequation time series models: vector auto regression (VAR), cointegration and error correction models, nonlinear time series models, state space time series models, Bayesian time series and forecasting.

FIN 7020: Theory of Financial Decision Making (3 credit hours)

This course has two main goals.  The first is to understand how well-run corporations create value.  The second is to develop a set of techniques for valuing capital investment projects in privately and publicly traded companies.  To support the first main goal, the course considers the theory of the firm and develops a set of principles concerning optimal allocation of costly resources and production levels in the face of market forces that depend on the nature of competition in input and output markets.  This section defines and identifies sources of market power that managers can exploit to create value.  With this analysis, students should be able to examine specific firms and the industries in which they operate to determine the firm’s short-term and long-term profitability and potential threats/risks.  To support the second main goal, the course (1) examines valuation techniques, (2) develops asset pricing models (the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT) in particular) to determine the appropriate required or opportunity cost of capital for discounting future cash flows, (3) considers basic risk management techniques, (4) examines how firms raise capital, (5) analyzes the effect of financing choices on shareholder wealth, firm value, risk, and tax payments, and (6) considers how corporations can design appropriate compensation schemes to induces effective managerial action and effort.  The course also defines real options and examines how real options are valued and affect capital budgeting decisions.  To support this task, the course examines the definition, use, and pricing of derivative securities such as financial options. 

FIN 7037: Fixed Income (3 credit hours)

This course examines fixed-income markets, with an emphasis on the pricing and risk of fixed income securities, derivatives, and portfolios. Bond immunization and trading strategies will be discussed with an in-depth coverage of both Treasury and Corporate Debt Securities. We will explain how Federal Reserve uses monetary policy to influence the term structure of interest rates. This course helps students to establish a solid foundation in understanding fixed-income securities and furthermore to apply such knowledge to real-world investment decisions in bond markets.

FIN 7042: Options and Futures (3 credit hours)

The principal objective of this course is to provide a detailed examination of options, futures, forwards, and swaps. By the end of the course students will have a good knowledge of how these contracts work, how they are traded, how they are used, and how they are priced. A major emphasis in the class will be on how derivative instruments are used by financial institutions in light of recent economic events.

FIN 7045: Portfolio Management (3 credit hours)

This course presents the mainstream and alternate view of portfolio management using research papers, articles, and materials from academics and the markets. Many of the concepts covered are covered in the body of knowledge leading to the CFA designation.

FIN 7052: Securities Trading and Markets (2 credit hours)

The focus of this course is the structure of financial markets and the trading of securities, primarily U.S. equities. In previous finance courses you likely assumed away the frictions involved in the trading process. In this course we will study those frictions. We will closely examine market structure, trade pricing rules, order submission strategies, trading costs, block trading, and market efficiency. The type of order submitted and the resolution of that order will depend, in part, on the structure of the market. The market structure is influenced heavily by government regulation and communications technology. Therefore, we will discuss the influence of the market structure on the trading process and the impact of recent SEC rule changes and alternative trading systems on competition in U.S. equity markets.

FIN 7054: Risk Management (3 credit hours)

This course examines the regulatory and risk management issues facing a variety of financial institutions (including depository institutions, insurance companies, investment banks, mutual funds, and pension funds). The course will start with some of the basic theories of financial intermediation to identify the various services financial institutions provide. We also will identify and analyze the key types of risks faced by financial institutions (focusing on interest rate risk, market risk, liquidity, and credit risk). With this as context, we will then examine the set of techniques available for measuring and managing these risks. We will focus on recent trends in off-balance sheet activities, securitization, and other financial innovations and will examine the causes, consequences, and suggested remedies of the 2008 financial crisis.        

IRM 7060: Advanced Financial Risk Management (3 credit hours)

The course focuses on techniques and methods applied to market risk, operational risk, and credit risk faced by a variety of different financial institution (including banks, insurance companies, hedge funds, etc…), given the regulatory environment in which they operate. 

Upon completion of the course, students should be able to

1.   Appropriately apply the standard set of metrics to quantify a variety of different types of risks faced by a variety of different types of financial institution, including market risk, credit risk, and operational risk.

2.  Delineate a set of alternative approaches for managing the set of identified and quantified risks.

3.   Develop and apply the appropriate concrete steps to implement each approach, while being able to identify potential limitations of specific techniques.        

3.   Analyze the trade-offs between each alternative approaches, being able to make and defend specific recommendations.

4.   Consider the management and regulatory environment of the institution and discuss strengths and weakness of those environments with respect to the incentive to manage financial risk effectively.    

IRM 7065:  Applied Projects in Financial Risk Management (3 credit hours)

This is a project- and case-based course designed to provide students seeking careers in financial risk management at major financial institutions (e.g., commercial banks, investment banks, insurance carriers (both P&C and life), hedge funds, pension funds, reinsurance firms, etc…) real-world projects that allow students to gains practical experience applying the tools and techniques developed in pre- and co-requisite courses (see below). Students will work on applied projects that utilize the material covered on the Financial Risk Manager (FRM) designation exams administered by the Global Association of Risk Professional (GARP), which includes foundational material from the economics, probability and statistics, and finance disciplines, as well as state-of-the-art techniques used by modern financial institutions.  

This course serves as the Capstone course for students in the MS-FIN program seeking to specialize in financial risk management.  

The course contains the following elements:

·         Lectures focused on issues with respect to application and implementation of specific financial risk management analytical tools (e.g., out-of-sample testing) to specific applied real world problems;

·         Analysis of relevant cases on corporate governance and risk management;

·         Project work conducted in collaboration with financial institutions that partner with the Carl H. Lindner III Center for Insurance and Risk Management in the Lindner College of Business at the University of Cincinnati. 

·         Students consult with these financial institutions to assess

1)  their risk management process and strategy,

2)  how they apply analytical techniques and models,

3)  the set of analytical tools and software they commonly use,

4)  the common problems encountered when applying theory to practice, and

5)  specific applied problems that require analysis.

·         Students work in small teams (2 to 3 members) to conduct analyses of real-work problems and present (written and oral) their analysis to industry partners. Team management and effective communication of insightful analysis is required for successful completion of the course requirements. 

·         Each student’s knowledge and contribution to an applied project will be assessed individually by the faculty member via oral examination and via peer-evaluation.  Industry partners will also take part in the evaluation of the quality of the analysis, recommendations, and professionalism.

·         The types of applied projects students will work may include:

            1)  Bank stress testing

            2)  Optimal hedging of market and interest rate risk exposures from annuities

            3)  Asset-liability management           

            4)  Value at Risk metrics of market risk

            5)  Quantification of the costs and transfer of emerging risks (e.g., cyber-terrorism)

            6)  Evaluation of the risk management processes and control of pension funds

            7)  Profit maximization subject to regulatory constraints    

            8)  Hedging stochastic volatility

ACCT 8089: Financial Statement Analysis (2 credit hours)       

This course is designed to understand financial statements and to use them to make sound decisions that you will be using in the real life. Managerial decision making and analysis will be emphasized. In order to successfully complete this course, one must already have at least a basic understanding of US Financial Accounting.        

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FRM-support Electives:

BANA 6025: Optimization Models (2 credit hours)

This course provides an introduction to optimization modeling using state-of-the-art software. Students will learn how to build linear, integer and nonlinear models for optimization applications. This course includes an introduction to basic solution techniques and post-optimization analysis including graphical approaches, duality and sensitivity analysis.

BANA 7035: Simulation Analysis (2 credit hours)

Probabilistic and statistical underpinnings of simulation modeling and analysis. Topics include advanced modeling techniques, advanced methods for modeling input processes, random-number generators, generating random variates and processes, design and analysis of simulation experiments, variance-reduction techniques, gradient estimation, and optimizing simulated systems.

BANA 7042: Statistical Modeling (2 credit hours)

Nonlinear regression and generalized linear model. Logistic regression for dichotomous and polytomous responses with a variety of links. Count data regression including Poisson and negative binomial regression. Variable selection methods. Graphical and analytic diagnostic procedures. Overdispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models.

BANA 7046: Data Mining I (2 credit hours)

This is a course in the statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Data Processing. Variable Selection for linear regression and generalized linear regression. Out-of-sample Cross Validation. Generalized Additive models. Nonparametric smoothing methods. Classification and Regression Tree. Neural Network. Monte Carlo Simulation.

BANA 7047: Data Mining II (2 credit hours)

This is a course is the follow-on course to BANA 7046 (Data Mining I).  It examines statistical data mining techniques with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Missing Data Imputation, Bootstrapping, Boosting and Multiple Additive Regression Trees, Bayesian Trees, Support Vector Machine, Discriminant Analysis, Cluster Analysis, Factor Analysis, Principle Component Analysis.

BANA 6037: Data Visualization (2 credit hours)

This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.  

FIN 7072: Behavioral Finance (2 credit hours)

Behavioral Finance considers the impact of human psychology on financial markets and corporate decision-making. Topics covered judgment, social and emotional biases and their effects on investor behavior and market outcomes. Corporate finance applications will also be included.

FIN 7046: Alternative Investments (2 credit hours)

The objective of this course is to provide the student with an introduction and understanding of the alternative investment universe and its many subcategories, including hedge funds, private equity and real assets. The class will strike a balance between academic evidence and real world pragmatism. Along the way, students will get the opportunity to hear several guest lecturers from the hedge fund, private equity, and real assets industry. It is expected that students would have an interest in alternative investments; however, no prior knowledge of alternative investments is required. Many concepts will build upon other traditional finance and investment courses.

FIN 7055: International Finance (2 credit hours)

This course will focus on the currency markets, international capital markets, the parity relationships which govern relative prices, and derivative securities used to manage foreign exchange and interest rate risk. We will first discuss the institutional organization of each market. Given the market structure, our aim is to understand how prices are determined in each market. We often rely on theoretical models to make predictions on price determination. Testing these models reveals regularities in market prices and unexplained phenomena. We can then examine how the policies of corporations, governments, and regulators are formed based upon these prices. In other words, we take an in depth look at how these markets function and their implications for market participants. A thorough comprehension of the function of these markets is necessary to make effective fund-raising and investment decisions.

STAT 6042: Survival Analysis and Logistic Regression (3 credit hours)

This course will begin with a detailed description of maximum likelihood. It will then discuss generalized linear models, including logistic and Poisson regression. Finally various topics in survival analysis will be covered: namely Kaplan-Meier curves and log-rank statistics, Weibull regression, and Cox proportional hazard regression. Examples from medicine and engineering will be given. SAS and S-plus statistical software will be used.

STAT 6045: Statistical Computing with SAS and S-plus (3 credit hours)

This course will cover the basics of using the SASand S-plus statistical software. Topics covered include: importing external files, subsetting and merging data files, performing statistical procedures, graphics, matrix calculations, and macros and functions.

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