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CIO Springer TAP Compal BES FCT
Schedule
TB4 Optimization approaches to financial and energy markets
[ORGANIZED SESSION]
Session organizer: Giorgio Consigli, University of Bergamo, Italy

Chair: Giorgio Consigli, University of Bergamo, Italy
Room 3.1.10

 

TB4.1
Insurance portfolios stress-testing by stochastic optimization

Giorgio Consigli (giorgio.consigli@unibg.it), Vittorio Moriggia (vittorio.moriggia@unibg.it)
The introduction of the Solvency II regulatory framework in 2011 and unprecendented property and casualty claims experienced in recent years by large insurance firms have motivated the adoption of risk-based capital allocation policies in the insurance sector. In this article we present the key features of a dynamic stochastic program leading to an optimal asset-liability management and capital allocation strategy by a large P/C insurance company and describe how from such formulation a specific, industry-relevant, stress-testing analysis can be derived. Throughout the article the investment manager of the insurance portfolio is regarded as the relevant decision maker: he faces exogenous constraints determined by the core insurance division and he is subject to the capital allocation policy decided by the management, consistently with the company’s risk exposure. A novel approach to stress-testing analysis by the insurance management, based on a recursive solution of a large-scale dynamic stochastic program is presented.

Keywords: Institutional ALM, multistage stochastic quadratic programming, property and casualty insurance

 

TB4.2
A Computational Method for Predicting the Entropy of Energy Market Time Series

Francesco Benedetto (francesco.benedetto@uniroma3.it), Gaetano Giunta (gaetano.giunta@uniroma3.it), Loretta Mastroeni (loretta.mastroeni@uniroma3.it)
This work introduces a new computational method for evaluating the predictability of energy market time series, by predicting the entropy of the series. According to conventional entropy-based analysis, high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, as happens in conventional computational minimization approaches. Preliminary results, applied to energy commodities, show the efficacy of the proposed method for application to energy market time series.

Keywords: energy market time series, entropy methods, computational predictive methods

 

TB4.3
Investigating volatility behaviour in energy commodity prices

Alessandra Amendola (alamendola@unisa.it), Vincenzo Candila (vcandila@unisa.it), Antonio Scognamillo (ascognamillo@unisa.it)
According to a consolidate literature, volatility is a central aspect in financial markets. In particular, modeling crude oil volatility is of substantial interest to both energy researchers and policy makers. The results of a GARCH model on crude oil future (COF) log returns show the existence of heteroskedasticity in the period 1998-2013. In particular, the estimated volatility experiences a peak during the U.S. latest economic crisis (2007-2009). In the same period, the Federal Reserve carried out an aggressive expansionary monetary policy in order to stimulate the U.S. economy. For this reason, investigating a possible impact of the monetary policy on the volatility is a relevant issue. The recently proposed GARCH-Midas model shows that the U.S. Effective Federal Funds rate does affect the variability of crude oil. Another relevant issue concerns the existence of a bubble in the COF prices and its possible effect on the volatility. Effectively, during the crisis, the prices first rapidly spike upwards and then fall down. A sup Augmented Dickey Fuller test provides strong evidences of a bubble, lasting about four months (April-August 2008). Interestingly, the expansionary monetary policy and the bubble are almost simultaneous. Our results suggest that the former affects volatility through the latter. In fact, considering separately the periods before and after the bubble, the impact of monetary policy on volatility changes, resulting greater in the second sub-sample.

Keywords: Volatility, GARCH-Midas, crude oil