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8011215 -
THEORY OF BANKING
(obiettivi)
he course will discuss some fundamental issues related to financial intermediation in contemporary economic systems. Why do financial intermediaries exist? What is the role they play and what are the consequences of their activities?
Preliminaries: What is a bank and what are her functions: a close look at the balance sheet of bank.
Part I
Introduction to time, uncertainty and liquidity. Asymmetric information problems: adverse selection and moral hazard
Microeconomic foundations for financial intermediation. Why do banks exist. Banks in Arrow-Debreu economies. Banks as pool of funds: Diamond-Dybvig (1983). Banks as delegated monitors: Diamond (1984).
Bank runs as outcomes of a self-fullfilling prophecy. Remedies to economic instability due to bank runs. The economic cycle and its effect on bank runs.
The effects of banks on financial markets: equilibria with credit rationing. Stiglitz-Weiss (1981). The role of collateral as an incentive device.
Part II
Banks can fail: history and institutions. A discussion on the emergence of a financial crisis: the causes, the consequences and policy issues related to a crisis. The trasmission mechanisms from the financial to the real sector: money view and credit view. The main features of the 2007-2009 financial crisis in comparison with past crises’ episodes: the causes, the transmission mechanisms, the consequences. Financial market regulation before and after the crisis of 2007-2009.
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CAMPIONI ELOISA
( programma)
Preliminaries: What is a bank and what are her functions: a close look at the balance sheet of banks.
Part I
Introduction to time, uncertainty and liquidity. (Chp. 2, Allen-Gale)
Introduction to asymmetric information problems. The case of Moral hazard (Introduction & Chapter 5, Salaniè)
Microeconomic foundations for financial intermediation. Why do banks exist. Banks in Arrow-Debreu. (Introduction & Chp. 1, Freixas-Rochet).
Banks as pool of funds: Diamond-Dybvig (1983). Bank runs as a sefl-fullfilling prophecy. The economic cycle and its effect on bank runs. (Chapter 3, Allen-Gale; Introduction, Freixas-Rochet).
Remedies to economic instability due to bank runs.
Banks as delegated monitors: Diamond (1984).
The effects of banks on financial markets: equilibria with credit rationing. Stiglitz-Weiss (1981) the example with moral hazard. The role of collateral as an incentive device (Bester-Hellwig).
Part II
Banks can fail: history and institutions. A discussion on the emergence of a financial crisis: the causes, the consequences and policy issues related to a crisis. (Chp. 1, Allen-Gale)
Financial market regulation as it has been designed before the current crisis.
Analysis of the features of the financial crisis of 2007-2009 in comparison with past crises’ episodes: the causes, the transmission mechanisms, the consequences.
A focus on the ongoing academic and policy debate on current issues in banking regulation.
 Main References
Freixas X. and J.-C. Rochet, Microeconomics of Banking, 1st Edition, MIT Press 1998
Allen F. and D. Gale, Understanding Financial Crises, Oxford University Press 2007
Salaniè B., The Economics of Contracts. A Primer, MIT press.
Tirole J., The Theory of Corporate Finance, Princeton University Press 2006
Supplementary readings
De Bonis R., La Banca, Le Bussole Carocci, 2008
Diamond- Dybvig, "Bank runs, deposit insurance and liquidity", JPE 2003
Diamond, "Financial intermediation and delegated monitoring", RES 1984
Diamond, "Financial intermediation as delegated monitoring: a simple example", 1996
Bester, "Screening vs rationing in credit markets with imperfect information", AER 1985
Stiglitz -Weiss, "Credit rationing in markets with imperfect information " , AER 1981
Bernanke, "Non-monetary effects of the financial crisis in propagation of the Great Depression", AER 1983
Caprio-Klingebiel, "Episodes of systemic and borderline financial crises", World Bank document, 2003 (available on World Bank website)
Reinhart-Rogoff, "From financial crash to debt crises", AER 2011
Cecioni-Ferrero-Secchi, "Unconventional monetary policy in theory and in practice", Questioni di Economia e Finanza (Occasional Papers) Bank of Italy, n.102, 2011 (available on the website of the Bank of Italy)
EXAM
The final exam of the course is a written exam, which will last at most 1h30 min. The students and the lecturers can voluntarily ask for an oral discussion. The oral discussion can contribute to the final grade for max 10 per cent (+ or - 3/30).
It will be divided into two sections: one related to the topics presented by Prof. E. Campioni and the other to the topics taught by prof. R. De Bonis.
The exam can be taken at most once for every session.
The pre-appello is an anticipation of the final exam, which can take place at the end of the course lessons. The pre-appello has an identical structure with respect to the final exam.
Those who hand in their exam on the pre-appello cannot sit in the summer exam, but can retake the exam in the fall. Those who decide to withdraw their exam in the pre-appello, can come back in the summer exam.
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SECS-P/01
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36
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Attività formative affini ed integrative
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ENG |
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8011288 -
FINANCIAL ECONOMETRICS
(obiettivi)
1. Introduction
Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering.
Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.
2. Volatility measurement and analysis
Autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
Generalized ARCH models, Integrated GARCH, Exponential GARCH models.
Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH 2.4 Realized volatility.
Risk measurement: Value at Risk and expected shortfall.
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PROIETTI TOMMASO
( programma)
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).
Main references
Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
Franke, J., Hardle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
Terence Mills and Raphael Markellos. The Econometric Modelling of Financial Time Series, 2007 Cambridge University Press.
McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
Tsay, R.S. (2010). Analysis of Financial Time Series, Third
Edition. Wiley.
Reading list
Andersen, T. and T. Bollerslev (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39, 885–905.
Andersen, T., T. Bollerslev, F. Diebold, and P. Labys (2003). Modeling and forecasting realized volatility. Econometrica 71, 579–625.
Andersen, T., T. Bollerslev, and F. X. Diebold (2010). Parametric and nonparametric volatility measurement.
In L. P. Hansen and Y. Ait-Sahalia (Eds.), Handbook of Financial Econometrics. Amsterdam: North-Holland.
Banerjee, A. and G. Urga (2005). Modelling structural breaks, long memory and stock market volatility: an overview. Journal of Econometrics 129(1-2), 1–34.
Barndorff-Nielsen, O. E. and N. Shephard (2007). Variation, jumps, market frictions and high frequency data in financial econometrics. In Advances in Economics and Econometrics. Theory and Applications, Ninth World Congress, pp. 328–372. Cambridge University Press.
Bollerslev, T., R. Y. Chou, and K. F. Kroner (1992). ARCH modelling in finance: a selective review of the theory and empirical evidence. Journal of Econometrics 52, 5–59.
Carstensen, K., K. Wohlrabe, and C. Ziegler (2010). Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production. CESifo Working Paper Series No. 3158.
Chen, S. (2005). A note on in-sample and out-of-sample tests for Granger causality. Journal of Forecasting 24(6), 453–464.
Clark, T. and M. McCracken (2001). Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics 105(1), 85–110.
Clark, T. and K. West (2006). Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. Journal of Econometrics 135(1-2), 155–186.
Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7, 174–196.
Diebold, F. and R. Mariano (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics 13(3), 253–263.
Fama, E. (1965). The behavior of stock market prices. Journal of Business 38, 34–105.
Garcia, R., E. Ghysels, and E. Renault (2010). The econometrics of option pricing. In L. P. Hansen and Y. Ait-Sahalia (Eds.), Handbook of Financial Econometrics, pp. 479–552. Amsterdam: North-Holland.
Ghysels, E., P. Santa-Clara, and Valkanov (2002). The MIDAS touch: Mixed data sampling regression models. Working paper, UNC and UCLA.
Ghysels, E., P. Santa-Clara, and R. Valkanov (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics 131(1-2), 59–95.
Ghysels, E., A. Sinko, and R. Valkanov (2007). MIDAS regressions: Further results and new directions.
Econometric Reviews 26(1), 53–90.
Harvey, D., S. Leybourne, and P. Newbold (1998). Tests for forecast encompassing. Journal of Business
& Economic Statistics 16(2), 254–259.
Hubrich, K. and K. West (2010). Forecast evaluation of small nested model sets. Journal of Applied Econometrics 25(4), 574–594.
Inoue, A. and L. Kilian (2005). In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23(4), 371–402.
Meese, R. and K. Rogoff (1983). Empirical exchange rate models of the seventies:: Do they fit out of sample? Journal of international economics 14(1-2), 3–24.
Poon, S.-H. and C. Granger (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature 41(2), 478–539.
Ren`o, R. (2010). Financial modelling II Lecture notes. Available at www.econ-pol.unisi.it/fm20.
Rogoff, K. and V. Stavrakeva (2008). The continuing puzzle of short horizon exchange rate forecasting.
NBER working paper.
Sarno, L., D. Thornton, and G. Valente (2005). Federal Funds Rate Prediction. Journal of Money, Credit & Banking 37(3), 449–472.
Schwert, G. W. and P. Seguin (1990). Heteroskedasticity in stock returns. Journal of Finance 45, 1129–1155.
Taylor, S. (1986). Modeling Financial Time Series. J. Wiley & Sons.
West, K. (2006). Forecast evaluation. Handbook of Economic Forecasting 1, 99–134.
West, K. and M. McCracken (1998). Regression-based tests of predictive ability. International Economic Review 39(4), 817–840.
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SECS-S/03
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36
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Attività formative caratterizzanti
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ENG |
