Corso di laurea magistrale - Area di Economia - Accesso libero con verifica di requisiti e preparazione in ingresso - Classe LM-16
Lingua: Inglese
Informazioni generali:
Descrizione e obiettivi formativi:
Il Corso, interamente tenuto in lingua inglese, assicura una formazione di alto livello nel campo della finanza quantitativa, un'area estremamente interessante e attuale nel settore finanziario, con un ampio spettro di applicazioni tecniche e finanziarie. Gli studenti acquisiscono competenze teoriche e pratiche di rilievo nel settore finanziario, quali la misurazione, l'analisi e la gestione del rischio nelle sue diverse accezioni, di mercato, finanziario, di credito e di gestione. Accanto agli aspetti giuridici, vengono curate anche le competenze in econometria e statistica, ivi compreso l'uso di software dedicati.
Il corso prevede insegnamenti in Introduzione al software statistico, Gestione del rischio e creazione di valore nell'attività bancaria, Asset Management, Teoria del diritto e dell'etica della finanza, Statistica, Matematica e Probabilità, Gestione del rischio finanziario, Econometria, Analisi delle serie temporali, Corporate Finance, Asset pricing, Gestione di titoli derivati.
Sbocchi professionali
Il Corso prepara risk manager, trader, asset manager, financial engineer, risk consultant, analisti quantitativi, esperti di software finanziari, che possono operare in banche, trading houses, società che gestiscono fondi pensionistici, compagnie di assicurazione, information provider focalizzate nella costruzioni di applicativi di gestione dedicati.
In dettaglio, il Corso prepara esperti con elevata conoscenza degli strumenti finanziari, anche complessi, e buona capacità di pricing degli strumenti finanziari (prodotti strutturati e derivati):
• gestori di soluzioni finanziarie complesse, con una elevata capacità di valutazione e misurazione del rischio sottostante ai prodotti finanziari, solide abilità nell’ambito delle ottimizzazioni matematiche necessarie per la costruzione del portafoglio, ampia capacità di sintesi e di gestione della complessità e dimestichezza con applicativi informatici di risk management ed asset management.
• gestori delle relazioni con la clientela di elevato standing, che sanno interpretare i fabbisogni della clientela e sanno lavorare nel campo della pianificazione finanziaria e nella gestione dei flussi di cassa, grazie alla loro capacità di sintetizzare le esigenze degli investitori in una proposta di investimento/finanziamento coerente. Hanno ottime capacità di gestione delle relazioni interpersonali. Sanno svolgere attività di Private Banking e consulenza agli investitori istituzionali.
Valutazione della didattica - Studenti
Anno accademico precedente
Condizione occupazionale (indicatori di efficacia e livello di soddisfazione dei laureandi):
http://statistiche.almalaurea.it/universita/statistiche/trasparenza?CODICIONE=0580207301700001
Riferimenti web e contatti
Sito Web: http://economia.uniroma2.it/master-science/financeandbanking
Coordinatore:
Prof. Rocco Ciciretti
Telefono: +39 06 7259 5645
Le informazioni sulle istruzioni per l'immatricolazione al Corso di Laurea Magistrale sono presenti al seguente link:
https://economia.uniroma2.it/master-science/financeandbanking/admission-application/
The course is divided into two blocks. Block 1: Matlab. Working with the MATLAB User Interface Variables and Commands Working with Vectors Working with Matrices Automating Commands with Scripts Dates and Times Working with Tabular Data Conditional Data Selection Working with Missing Data Writing Functions Increasing Automation with Programming Constructs Fitting Models to Empirical Data Troubleshooting Code Block 2: Static Regression. Simple linear regression model: • OLS estimators: derivation through first order conditions. • Definition and interpretation of the coefficient of determination. • Unbiasedness of OLS estiamtors: theory and practice (with Matlab). • Conditional variance of OLS estiamtors: theory and practice (with Matlab). • Unbiased estimator of error variance. • Statistical inference: hypothesis testing and t-statistic. • Statistical inference: the Capital Asset Pricing Model and the beta of a stock. Multiple linear regression model: • Recap of matrix algebra and gradient of a function. • OLS estimators: derivation through first order conditions. • Unbiasedness of OLS estiamtors.
Univariate Time Series: Stationary time series: Basic concepts. Stationarity, Total and partial autocorrelation, Ergodicity, Linear stationary processes, ARMA models, Outliers, Forecasting. Nonstationary time series: ARIMA models, The Beveridge-Nelson Trend-Cycle decomposition, Seasonality, Statistical inference: Estimation, Identification, Diagnostic checking. Unit roots in economic and financial time series: Deterministic trends vs. random walks, Unit-roots tests, Impulse response function and measures of persistence Multivariate Time Series: Stationary and Ergodic Multivariate Time Series Multivariate Wold Representation Vector Auto-Regressive (VAR) Models Identification and Estimation of VAR models Forecasting Structural VAR Models Impulse Response Functions Forecast Error Variance Decompositions Shocks Identification Using the Choleski Factorization The Cointegrated VAR Maximum Likelihood Inference on the Cointegrated VAR The Common Trends Representation.
Based on lectures and the study of relevant cases, the course focuses on the main topics of corporate finance such as: CAPITAL BUDGETING VALUING BONDS AND STOCKS RISK AND THE COST OF CAPITAL CAPITAL STRUCTURE AND CORPORATE VALUATION
AN INTRODUCTION TO FIXED INCOME MARKETS Government Debt, Money Market, Repos, MBS, ABS, Derivatives BASICS OF FIXED INCOME SECURITIES Discount Factors, Interest Rates, The Term Structure of Interest Rates INTEREST RATE RISK MANAGEMENT Duration, Immunization, Asset-Liability Management INTEREST RATE DERIVATIVES Forward, Swaps, Futures, Options INFLATION AND MONETARY POLICIES The role of the central bank. Forecasting and interpreting the term structure. TERM STRUCTURE MODELS IN CONTINUOUS TIME Vasicek and Cox-Ingersoll-Ross models
The course is based on the ARPM Marathon, available through the ARPM interactive learning platform, and it covers the last two out of the four learning modules: 1) Financial Engineering for Investment 2) Data Science for Finance 3) Quantitative Risk Management 4) Quantitative Portfolio Management The first two modules will be covered in the course "Advanced Topics in Finance and Insurance I".
The course provides an overview of quantitative methods for pricing and hedging credit risk. The course is divided into two parts. The first part is meant to provide the students with the mathematical tools needed for quantitative models of credit risk. The second part applies the tools to mainstream models, in particular on the two main typology: structural models and reduced form models. Part one (mathematical tools): The Wiener process. Martingales. Geometric Brownian Motion. The Black-Scholes formula. The Bernoulli and binomial distribution. The exponential distribution. The Poisson process. Compound Poisson process. Doubly stochastic Poisson processes. Jump-diffusion models.Part two (applications to credit risk): Structural models: Merton approach. Modigliani-Miller economy (review). Pricing of defaultable bonds in structural models. The relation between options and corporate liabilities. Reduced form models: intensity of default. Pricing defaultable ZCB's. The case with no recovery. The FRMV assumption. The general case. Intensity of default and risk-neutrality. The BTP-Bund spread. CDS pricing.
PROGRAM 1) Introduction to Derivatives (main contract types, the underlying assets, derivatives markets, applications) 2) The Forward: - definition, main features, application - Pricing and the hypothesis of no-arbitrage - The value of the forward contract 3) The Future: - definition, main features, application - Pricing and market value 4) The Forward Rate Agreement (FRA): - definition, main features, application - Pricing and market value - The market makers and the Double way quote 5) The Swap - definition - The Interest Rate Swap (IRS): main features, application and pricing - The currency swap 6) The Options - Definition - Option call: main features, pay-off and application - Option put: main features, pay-off and application - The option pricing (basic elements): * Intrinsic value and temporal value * Pricing limit: the maximum and minimum price limits - Option trading strategies 7) Cap, Floor and Collar - Cap: main features, pay-off and application - Floor: main features, pay-off and application - Collar: main features, pay-off and application 8) The Exotic Options - Definition - Analysis of the main contract types (Boston option, Bermuda option, Forward start option, compound option, as you like it option, barrier option, binary option, look back option, asian option, rainbow option, basket option) 9) The Structured Products - Definition - Analysis of the main contract types (Equity linked, reverse convertible, reverse floaters, credit default obligations, etc.) MATERIALS J.C.Hull, Option, Futures and Other derivatives, Pearson-Prentice Hall (last edition). Slides used in class can be downloaded from the web-pages of the courses (they will be available during the course).
Section 1: Market and Securities’ Characteristics. Markets and Indexes Definitions; Prices and Returns of Securities; Prices and Returns of a Portfolio; Practice 1: Compute the Stocks and Portfolio’s Return/Risk Prospects. Section 2: Efficient Portfolios and Efficient Frontier. Building the Efficient Frontier; Shape of the Efficient Frontier; Extend the Efficient Frontier allowing for Risk Free Rate and Short Selling; Return Maximization Problem; Practice 2: Building the Efficient Frontier. Section 3: Single Index Model. Inputs for the Portfolio Analysis; Single Index Model: Overview and Characteristics; Estimating Stocks and Portfolios’ Market Betas; Building the Frontier with the Single Index Model; Practice 3: Estimating the CAPM and the FFC Single Stock Level and for a Portfolio. Section 4: Standard and Non-Standard Capital Assets Pricing Model. Capital Asset Pricing Model by means an Intuitive Approach; Capital Asset Pricing Model by means a Rigorous Approach; Fama-French e Carhart Multifactor Model (FFC); CSR Risk-Factors and the Investors’ Preferences for Responsible Investment; Corporate Social Responsibility, Responsible Investments on the Financial Markets; Responsible Fama-French-Carhart Model (RFFC); Standard Test for Equilibrium Models; Black, Jensen e Sholes Approach; Fama MacBeth Approach; Practice 4: Estimating the Factors’ Risk-Premia by using the Fama-MacBeth Approach. Section 5: Efficient Markets Hypothesis. Introduction to the Efficient Market Hypothesis (EMH); Three forms of Market Efficiency; Semi-Strong Market Efficiency and the Event Study Approach; Tests for the Semi-Strong Market Efficiency; Practice 5: Estimating the (C)AR by using the Event Study Approach.
Part 1 (Tommaso Proietti) 1. Introduction 1.1 Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. 1.2 Stochastic processes: stationarity, purely random processes (white noise). 1.3 Random walks and martingales. 1.4 Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing. 2. Volatility measurement and analysis: 2.1 Autoregressive Conditional Heteroscedasticity (ARCH): model specifi cation, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean. 2.2 Generalized ARCH models, Integrated GARCH, Exponential GARCH models. 2.3 Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH 2.4 Realized volatility. 2.5 Risk measurement: Value at Risk and expected shortfall. Part 2 (Marianna Brunetti) 1. MIDAS: MIxed DAta Sampling. Introduction (Ghysels et al., 2002, 2006). Forecasting with mixed (and high) frequency data (Ghysels et al., 2007). 2. Forecasting accuracy. Introduction: schemes, number of observations, why out of sample (Chen, 2005; Inoue and Kilian, 2005), measures of accuracy (MSFE, MAFE, forecast encompassing in (Harvey et al., 1998). Comparing small number of models (West and McCracken, 1998), nested models (Clark and West, 2006; Clark and McCracken, 2001; Hubrich and West, 2010), non-nested models (Diebold and Mariano, 1995). Comparing large number of models (West, 2006). Applications: Business Cycle (Carstensen et al., 2010), Exchange Rates (Rogoff and Stavrakeva, 2008), Interest rates (Sarno et al., 2005; Meese and Rogoff, 1983). 3. Econometrics with option prices (Garcia et al., 2010).
The course examines the main functions of the investment banking activity. Based on relevant cases study the course focuses on the main topics in investment banking such as: • Corporate Valuation • Debt and equity offerings • Merchant banking • Merger and acquisition • Credit and financing
1.Reminder on Compound Interest 2.Survival probability 3.Life Insurance contracts 4.Life Annuities 5.Net Premiums and Reserve Calculations 6.Multiple Life Insurance 7.Equity linked iand Unit linked Insurance
Review of basic concepts on financial marlets and banking intermediaries Market-based vs bank-based economies Elements of Teoru of Banking Finance and Growth M&As in the banking sector Credit risk - the scoring models Other measures of bank risks Individual bank runs
1. The stages of Portfolio Construction: - Strategic Asset allocation - Tactical Asset Allocation - Stock/Bond Selection 2. Benchmark 3. Strategic Asset allocation: - Naive Portfolio Formation Rule - Markowitz Model - Modello di Markowitz: Limits - Estimation Error - Heuristic Techniques: Additional Constraints e Resampling(TM) - Bayesian Techniques: Black-Litterman Model 4. Tactical Asset Allocation 5. Fund Valuation and Selection: - Risk Measures - Fund Management: passive versus active - Risk Adjusted Performance Measure -The Return Based Style Analysis
The field of asset pricing aims to explain why financial assets have the returns they do. While we still do not have definite answers, the field developed thus far is insightful. This course covers some of the field’s milestones. Our starting point is the assumption of competitive markets. We abstract away all frictions and build models that focus on risk. The rationale is that risk is the most critical factor in determining expected returns. Despite the elegance of the models, they fail to explain the data. Dividends (or consumption) are dramatically less volatile than stock prices, implying that the only way the models can match the data is if we assume investors have unreasonably high levels of risk aversion. We will discuss some extensions that attempt to bridge the gap between the data and the theory. We then take a step backward and abstract from risk as well. We want to see how far we can go if we only require that investors prefer more to less. This so-called no-arbitrage theory will allow us to price derivative securities. Again, we will visit the data and see how well the classic no-arbitrage models perform. Next, we will visit a purely statistical approach to asset pricing, the so-called Fama-French factors. Next, we look at some basic extensions of the theory that take into account investors’ psychology (so-called behavioral finance), asymmetric information (so-called wisdom of the crowd), and bubbles (this setup will also help us understand how fiat money, in a rational environment, can emerge as a feasible storage of value). To conclude the course, we learn how assets are priced in actual stock exchanges (so-called price discovery). With the help of a simple model that considers the price discovery process, we will gain insight into the real-world stock trading environment.
