Mathematical and Statistical Methods for Actuarial Sciences by Marco Corazza, Claudio Pizzi (eds.) PDF

By Marco Corazza, Claudio Pizzi (eds.)

ISBN-10: 3319024981

ISBN-13: 9783319024981

ISBN-10: 331902499X

ISBN-13: 9783319024998

The interplay among mathematicians and statisticians has been proven to be an effective process for facing actuarial, assurance and financial difficulties, either from an educational point of view and from an operative one. the gathering of unique papers offered during this quantity pursues accurately this objective. It covers a wide selection of matters in actuarial, assurance and finance fields, all handled within the mild of the profitable cooperation among the above quantitative methods.

The papers released during this quantity current theoretical and methodological contributions and their purposes to actual contexts. With admire to the theoretical and methodological contributions, the various thought of components of research are: actuarial versions; substitute checking out methods; behavioral finance; clustering thoughts; coherent and non-coherent hazard measures; credits scoring techniques; information envelopment research; dynamic stochastic programming; financial contagion types; financial ratios; clever financial buying and selling structures; combination normality ways; Monte Carlo-based tools; multicriteria equipment; nonlinear parameter estimation options; nonlinear threshold types; particle swarm optimization; functionality measures; portfolio optimization; pricing tools for established and non-structured derivatives; chance administration; skewed distribution research; solvency research; stochastic actuarial valuation equipment; variable choice types; time sequence research instruments. As regards the purposes, they're relating to actual difficulties linked, one of the others, to: banks; collateralized fund responsibilities; credits portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market speculation; alternate markets; financial time sequence; firms; hedge money; non-life insurance firms; returns distributions; socially accountable mutual cash; unit-linked contracts.

This booklet is geared toward teachers, Ph.D. scholars, practitioners, pros and researchers. however it can be of curiosity to readers with a few quantitative historical past knowledge.

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Extra resources for Mathematical and Statistical Methods for Actuarial Sciences and Finance

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The model is specified under a probability Q, which is assumed to be an equivalent martingale measure. Hence, under Q, the price of any traded security is given by its expected discounted cash-flows (see [8]). Discounting is performed at the riskfree rate r, assumed here to be constant. We use a geometric Brownian motion to model the Q-dynamics of the reference asset price, defined as dSt = r dt + σ dWt , St with (Wt ) a Wiener process, σ > 0 the volatility parameter and S0 > 0 given. We denote by τ the time of death of the policyholder and by m(t) its deterministic force of mortality.

Assessing the least squares Monte Carlo approach to American option valuation. Rev. Deriv. Res. 7(2), 129–168 (2004) 16. : American option pricing using simulation and regression: numerical convergence results. H. ) Topics in Numerical Methods for Finance. Springer Proceedings in Mathematics & Statistics 19, pp. 57–94 (2012) 17. : Option pricing: mathematical models and computation. Oxford Financial Press (1993) Dynamic Tracking Error with Shortfall Control Using Stochastic Programming Diana Barro and Elio Canestrelli Abstract In this contribution we tackle the issue of portfolio management combining benchmarking and risk control.

In the numerical experiments we choose the following set of basic parameters: the maturity T = 20, the risk-free rate r = 4%, the initial value of the reference asset S0 = 100 and its volatility σ = 20%. R. Bacinello et al. for i=d,w, where F0 is the principal of the contract. The European value of the contract admits a closed form expression (see [6, 7]) given by V0E = U ∗E = F0 S0 + S0 T 0 d s put(t, S0 er t ) t px m(t)dt + put(T, S0 er T ) T px , −c2 z where z py = e− 0 m(y−x+u)du = e−c1 ((y+z) 2 −y 2 ) and put(t, K) is the time 0 value of the European put with maturity t, strike K, underlying S in the Black-Scholes model (see [5]).

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Mathematical and Statistical Methods for Actuarial Sciences and Finance by Marco Corazza, Claudio Pizzi (eds.)

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