arXiv subjects

Guglielmo D'Amico

Publications and source records attributed to Guglielmo D'Amico.

At least 19 recordsLinked to original sources

arXiv2025

Instantaneous Failure, Repair and Mobility Rates for Markov Reliability Systems: A Wind-Farm application

The Rate of Occurrence of Failures (ROCOF) is a widely utilized indicator for assessing a system's performance over time, yet it does not fully disclose the instantaneous behavior of a system. This paper introduces new measures to complement the ROCOF, providing a more comprehensive understanding of system reliability, particularly for Markov systems. We define the Rate of Occurrence of Repairs (ROCOR), which quantifies the system's instantaneous tendency to transition from failure to working states, and the Rate of Inoccurrence (ROI), which measures the propensity to remain within the current subset of states (either working or failure) without transitioning out. Explicit expressions for the computation of these rates are derived for Markov systems. Furthermore, a Total Mobility Rate (TMR) is proposed, integrating these individual rates to capture the overall dynamism of the system. The utility of these new indicators is demonstrated through a significant real-world application to wind farm management. The results from the wind farm study show that ROCOR, ROI, and TMR, when used in conjunction with ROCOF, reveal nuanced operational dynamics and reliability characteristics that are not discernible from static measures like Weibull parameters or ROCOF alone. These indicators can distinguish between sites with similar long-term wind profiles by identifying different "reliability logics," such as persistence-driven versus transition-driven behaviors. This enriched, time-dependent perspective provides valuable information for maintenance scheduling, operational strategies, and risk assessment, ultimately enhancing the ability to manage complex systems effectively.

eess.SY↗

arXiv2025

Modelling a storage system of a wind farm with a ramp-rate limitation: a semi-Markov modulated Brownian bridge approach

We propose a new methodology to simulate the discounted penalty applied to a wind-farm operator by violating ramp-rate limitation policies. It is assumed that the operator manages a wind turbine plugged into a battery, which either provides or stores energy on demand to avoid ramp-up and ramp-down events. The battery stages, namely charging, discharging, or neutral, are modeled as a semi-Markov process. During each charging/discharging period, the energy stored/supplied is assumed to follow a modified Brownian bridge that depends on three parameters. We prove the validity of our methodology by testing the model on 10 years of real wind-power data and comparing real versus simulated results.

stat.AP↗

arXiv2021

Randentropy: a software to measure inequality in random systems

The software Randentropy is designed to estimate inequality in a random system where several individuals interact moving among many communities and producing dependent random quantities of an attribute. The overall inequality is assessed by computing the Random Theil's Entropy. Firstly, the software estimates a piecewise homogeneous Markov chain by identifying the changing-points and the relative transition probability matrices. Secondly, it estimates the multivariate distribution function of the attribute using a copula function approach and finally, through a Monte Carlo algorithm, evaluates the expected value of the Random Theil's Entropy. Possible applications are discussed as related to the fields of finance and human mobility

q-fin.ST↗

arXiv2020

A micro-to-macro approach to returns, volumes and waiting times

Fundamental variables in financial market are not only price and return but a very important role is also played by trading volumes. Here we propose a new multivariate model that takes into account price returns, logarithmic variation of trading volumes and also waiting times, the latter to be intended as the time interval between changes in trades, price, and volume of stocks. Our approach is based on a generalization of semi-Markov chains where an endogenous index process is introduced. We also take into account the dependence structure between the above mentioned variables by means of copulae. The proposed model is motivated by empirical evidences which are known in financial literature and that are also confirmed in this work by analysing real data from Italian stock market in the period August 2015 - August 2017. By using Monte Carlo simulations, we show that the model reproduces all these empirical evidences.

q-fin.ST↗

arXiv2020

Confidence sets for dynamic poverty indexes

In this study, we extend the research on the dynamic poverty indexes, namely the dynamic Headcount ratio, the dynamic income-gap ratio, the dynamic Gini and the dynamic Sen, proposed in D'Amico and Regnault (2018). The contribution is twofold. First, we extend the computation of the dynamic Gini index, thus the Sen index accordingly, with the inclusion of the inequality within each class of poverty where people are classified according to their income. Second, for each poverty index, we establish a central limit theorem that gives us the possibility to determine the confidence sets. An application to the Italian income data from 1998 to 2012 confirms the effectiveness of the considered approach and the possibility to determine the evolution of poverty and inequality in real economies.

econ.EM↗

arXiv2020

A review of the Dividend Discount Model: from deterministic to stochastic models

This chapter presents a review of the dividend discount models starting from the basic models (Williams 1938, Gordon and Shapiro 1956) to more recent and complex models (Ghezzi and Piccardi 2003, Barbu et al. 2017, D'Amico and De Blasis 2018) with a focus on the modelling of the dividend process rather than the discounting factor, that is assumed constant in most of the models. The Chapter starts with an introduction of the basic valuation model with some general aspects to consider when performing the computation. Then, Section 1.3 presents the Gordon growth model (Gordon 1962) with some of its extensions (Malkiel 1963, Fuller and Hsia 1984, Molodovsky et al. 1965, Brooks and Helms 1990, Barsky and De Long 1993), and reports some empirical evidence. Extended reviews of the Gordon stock valuation model and its extensions can be found in Kamstra (2003) and Damodaran (2012). In Section 1.4, the focus is directed to more recent advancements which make us of the Markov chain to model the dividend process (Hurley and Johnson 1994, Yao 1997, Hurley and Johnson 1998, Ghezzi and Piccardi 2003, Barbu et al. 2017, D'Amico and De Blasis 2018). The advantage of these models is the possibility to obtain a different valuation that depends on the state of the dividend series, allowing the model to be closer to reality. In addition, these models permit to obtain a measure of the risk of the single stock or a portfolio of stocks.

q-fin.GN↗

arXiv2019

A copula based Markov Reward approach to the credit spread in European Union

In this paper, we propose a methodology based on piece-wise homogeneous Markov chain for credit ratings and a multivariate model of the credit spreads to evaluate the financial risk in European Union (EU). Two main aspects are considered: how the financial risk is distributed among the European countries and how large is the value of the total risk. The first aspect is evaluated by means of the expected value of a dynamic entropy measure. The second one is solved by computing the evolution of the total credit spread over time. Moreover, the covariance between countries' total spread allows understand any contagions in EU. The methodology is applied to real data of 24 countries for the three major agencies: Moody's, Standard and Poor's, and Fitch. Obtained results suggest that both the financial risk inequality and the value of the total risk increase over time at a different rate depending on the rating agency and that the dependence structure is characterized by a strong correlation between most of European countries.

q-fin.CP↗

arXiv2018

Indexed Markov Chains for financial data: testing for the number of states of the index process

A new branch based on Markov processes is developing in the recent literature of financial time series modeling. In this paper, an Indexed Markov Chain has been used to model high frequency price returns of quoted firms. The peculiarity of this type of model is that through the introduction of an Index process it is possible to consider the market volatility endogenously and two very important stylized facts of financial time series can be taken into account: long memory and volatility clustering. In this paper, first we propose a method for the optimal determination of the state space of the Index process which is based on a change-point approach for Markov chains. Furthermore we provide an explicit formula for the probability distribution function of the first change of state of the index process. Results are illustrated with an application to intra-day prices of a quoted Italian firm from January $1^{st}$, 2007 to December $31^{st}$ 2010.

q-fin.ST↗

arXiv2017

A new approach to the modeling of financial volumes

In this paper we study the high frequency dynamic of financial volumes of traded stocks by using a semi-Markov approach. More precisely we assume that the intraday logarithmic change of volume is described by a weighted-indexed semi-Markov chain model. Based on this assumptions we show that this model is able to reproduce several empirical facts about volume evolution like time series dependence, intra-daily periodicity and volume asymmetry. Results have been obtained from a real data application to high frequency data from the Italian stock market from first of January 2007 until end of December 2010.

q-fin.ST↗

arXiv2017

Multi-state models for evaluating conversion options in life insurance

In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the ageing effect. The value of the conversion option is evaluated after the formal description of this contract.

q-fin.PR↗

arXiv2016

Generalized semi-Markovian dividend discount model: risk and return

The article presents a general discrete time dividend valuation model when the dividend growth rate is a general continuous variable. The main assumption is that the dividend growth rate follows a discrete time semi-Markov chain with measurable space. The paper furnishes sufficient conditions that assure finiteness of fundamental prices and risks and new equations that describe the first and second order price-dividend ratios. Approximation methods to solve equations are provided and some new results for semi-Markov reward processes with Borel state space are established. The paper generalizes previous contributions dealing with pricing firms on the basis of fundamentals.

q-fin.MF↗

arXiv2015

Tornadoes and related damage costs: statistical modeling with a semi-Markov approach

We propose a statistical approach to tornadoes modeling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modeling the tornadoes intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornadoes intensity into six states, it is possible to model the tornadoes intensity by using Markov and semi-Markov models. We demonstrate that the semi-Markov approach is able to reproduce the duration effect that is detected in tornadoes occurrence. The superiority of the semi-Markov model as compared to the Markov chain model is also affirmed by means of a statistical test of hypothesis. As an application we compute the expected value and the variance of the costs generated by the tornadoes over a given time interval in a given area. he paper contributes to the literature by demonstrating that semi-Markov models represent an effective tool for physical analysis of tornadoes as well as for the estimation of the economic damages to human things.

physics.ao-ph↗

arXiv2014

Operational risk of a wind farm energy production by Extreme Value Theory and Copulas

In this paper we use risk management techniques to evaluate the potential effects of those operational risks that affect the energy production of a wind farm. We concentrate our attention on three major risk factors: wind speed uncertainty, wind turbine reliability and interactions of wind turbines due mainly to their placement. As a first contribution, we show that the Weibull distribution, commonly used to fit recorded wind speed data, underestimates rare events. Therefore, in order to achieve a better estimation of the tail of the wind speed distribution, we advance a Generalized Pareto distribution. The wind turbines reliability is considered by modeling the failures events as a compound Poisson process. Finally, the use of Copula able us to consider the correlation between wind turbines that compose the wind farm. Once this procedure is set up, we show a sensitivity analysis and we also compare the results from the proposed procedure with those obtained by ignoring the aforementioned risk factors.

physics.soc-ph↗

arXiv2013

Forecasting wind speed financial return

The prediction of wind speed is very important when dealing with the production of energy through wind turbines. In this paper, we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model that has been shown to be able to reproduce accurately the statistical behavior of wind speed. The model is used to forecast, one step ahead, wind speed. In order to check the validity of the model we show, as indicator of goodness, the root mean square error and mean absolute error between real data and predicted ones. We also compare our forecasting results with those of a persistence model. At last, we show an application of the model to predict financial indicators like the Internal Rate of Return, Duration and Convexity.

physics.data-an↗

arXiv2013

Reliability measures for indexed semi-Markov chains applied to wind energy production

The computation of the dependability measures is a crucial point in the planning and development of a wind farm. In this paper we address the issue of energy production by wind turbine by using an indexed semi-Markov chain as a model of wind speed. We present the mathematical model, we describe the data and technical characteristics of a commercial wind turbine (Aircon HAWT-10kW). We show how to compute some of the main dependability measures such as reliability, availability and maintainability functions. We compare the results of the model with real energy production obtained from data available in the Lastem station (Italy) and sampled every 10 minutes.

physics.data-an↗

arXiv2013

Wind speed forecasting at different time scales: a non parametric approach

The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed, to forecast wind speed one step ahead for different time scales and for very long time horizon maintaining the goodness of prediction. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones and we compare our forecasting results with those of a persistence model.

physics.data-an↗

arXiv2013

Multivariate high-frequency financial data via semi-Markov processes

In this paper we propose a bivariate generalization of a weighted indexed semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that financial returns are described by a weighted indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able to reproduce important stylized facts of financial time series like the persistence of volatility and at the same time it can reproduce the correlation between stocks. The model is applied to data from Italian stock market from 1 January 2007 until the end of December 2010.

q-fin.ST↗

arXiv2013

Performability analysis of the second order semi-Markov chains: an application to wind energy production

In this paper a general second order semi-Markov reward model is presented. Equations for the higher order moments of the reward process are presented for the first time and applied to wind energy production. The application is executed by considering a database, freely available from the web, that includes wind speed data taken from L.S.I. - Lastem station (Italy) and sampled every 10 minutes. We compute the expectation and the variance of the total energy produced by using the commercial blade Aircon HAWT - 10 kW.

physics.data-an↗