Ricevimento e Altre Informazioni

Orario di ricevimento

Ottobre 2017

Martedi: 3, 10, 17, 23 h. 16.00

Settembre 2017

Martedi 26: h 16.00.

Mercoledi 20: h. 11.30; Lunedi 11: h. 17.30; Mercoledi 6 h.11.30

Luglio 2017: Martedi 18 luglio h. 11.30. (Il ricevimento riprendera poi a Settembre); Giovedi 13: h. 11.30; Lunedi 3: h. 16.30

Giugno 2017: Lunedi 26: h. 10.30; Martedi 20: h. 16.30; Giovedi 8: h. 12.00

Maggio 2017: Martedi 30: h. 17.00; Giovedi 18: h. 16.30; Mercoledi 10: h 10.30; Giovedi 4: h 16.30

Aprile 2017: Venerdi 28: h. 16.00; Giovedi 20: h 16.30; 

Marzo 2017:  Martedi: 28, 21, 14, 7;  h. 16.30

Febbraio 2017: Martedi 28/02 h. 16.30; Lunedì 20/2 h. 16.30; Martedi 14/02 h. 16.30; Martedi 7/02 h. 16.30

Gennaio 2017: Martedi 31/01 h. 12.00; Martedi 24/01 h. 16.30; Mercoledi 18/01 h. 16.30; Giovedi 12/01 h. 16.30

A. A. 2010 / 2011
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 64
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 2
Ore: 16
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
Secondo Semestre
SSD: SECS-S/01
CFU: 2
Ore: 16
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2011 / 2012
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 64
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2012 / 2013
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2013 / 2014
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2014 / 2015
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2015 / 2016
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 8
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2016 / 2017
Primo Semestre
SSD: SECS-S/06
CFU: 8
Ore: 16
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 48
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 32
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
A. A. 2017 / 2018
Primo Semestre
SSD: NN
CFU: 2
Ore: 15
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 8
Ore: 45
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 45
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa
SSD: SECS-S/06
CFU: 6
Ore: 45
Dipartimento: Dipartimento di Studi per l'Economia e l'Impresa

Pubblicazioni

Ricerca

A Gentle Introduction to Value at Risk. This paper is an introduction to the measurement of market risk in financial markets, with examples drawn mainly from commodity markets. In particular, we present the concept of VaR, its limits, the problems related to its estimation and backtesting. This is done at single asset and at portfolio level. Issues related to estimation error, measurement of portfolio risk contribution and how to cope with derivative positions are also considered. Available at SSRN: https://ssrn.com/abstract=2942138
A Gentle Introduction to Counterparty Credit Risk. In this paper we introduce the reader to the basic tools for the computation of Counterparty Credit Risk such as Credit Value Adjustment and Debt Value Adjustment. We also present the effect of mitigating clauses, like netting and collateral, in reducing the credit exposure. Detailed numerical examples are presented with reference to commodity derivatives. Available at SSRN: http://ssrn.com/abstract=2816355
Electricity Forward Curves with Thin Granularity. We put forward a constructive definition of electricity forward price curve with cross-sectional timescale encompassing hourly frequency upward. The curve is jointly consistent to both risk-neutral market information, as represented by base load and peak load futures quotes, and historical market information, as mirrored by periodical patterns exhibited by time series of day-ahead prices. On a methodological ground, we combine non-parametric filtering with monotone convex interpolation in a way that the resulting forward curve is path wise smooth and monotonic, cross-sectionally stable, and time local. On an empirical ground, we exhibit these features in the joint context of EPEX Spot and EEX Derivative markets. A back testing analysis assesses the relative quality of our forward curve estimate compared to the benchmark market model of Benth et al. (2007). Available at SSRN: http://ssrn.com/abstract=2777990. Published version available on European Journal of Operational Research
Integrated Structural Approach to Counterparty Credit Risk with Dependent Jumps. This paper proposes an integrated pricing framework for CVA: the model is based on a structural approach which uses correlated Lévy processes with idiosyncratic and systematic components; the numerical scheme, instead, efficiently combines Monte Carlo simulation and Fourier transform based methods. The framework is sufficiently flexible in incorporating a number of mitigating clauses, such as netting and collateral provisions. We illustrate the tractability and the performance of the proposed numerical scheme, and analyse the effects originated by right-way and wrong-way risk under different assumptions related to the parameters controlling collateral and netting agreements. Available at SSRN: http://ssrn.com/abstract=2706416
Default Risk Premium in Credit and Equity Market: A New Approach for Structural Model Estimation . We propose a novel methodological approach to estimate a corporate structural model, by using data from credit and stock market, and we reconstruct the dynamics of the market value of assets and debt, and the default boundary, for a sample of non-financial firms. We exploit our results to extract the default risk premium, which combines the risk-neutral and the real-world measure of default probability. We show that the equity and the credit market exhibit a relationship with the default risk premium which is opposite to each other, by implementing a long-short portfolio strategy based on the default risk premium, which generates significant performance. Therefore, we argue that the 'distress puzzle', that is the counterintuitive negative relationship between default risk and stock return, can be solved, if the credit and the equity market securities are related through a default risk indicator, resulting from an appropriate structural model estimation, using only market data. Available at SSRN: http://ssrn.com/abstract=2611984
Approximated Pricing of Swaptions in General Interest Rate Models. We propose new bounds on the prices of European-style swaptions for a wide class of interest rate models. These bounds are computable whenever the joint characteristic function of the state variables is known in closed form or can be obtained numerically via some efficient procedure. In particular our lower bound involves the computation of one dimensional Fourier transform independently from the swap length. We also show that methods put forward by Singleton and Umantsev (2002) and Kim (2012) are particular cases of our general framework. In addition, we control the error of our method by providing a new upper bound on swaption price applicable to all linear-quadratic models. Finally the lower bound can be used as a control variable to reduce the confidence interval of the Monte Carlo technique. We test our bounds on different affine models, also allowing for jumps, and on a 2-factors quadratic Gaussian model. The bounds are found to be accurate and computationally efficient. Available at SSRN: http://ssrn.com/abstract=2660696. Published version available on Quantitative Finance