A reduced lattice model for option pricing under regime-switching

Costabile, Massimo; Leccadito, Arturo; Massabó, Ivar; Russo, Emilio

doi:10.1007/s11156-013-0357-9

Cited by 16 publications

(16 citation statements)

References 28 publications

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“…We apply the lattice approach already presented in Costabile et al (2014) to develop the discrete version of the continuous time framework detailed in Section 2. The proposed model takes into account the fact that the interest rate and the volatility may switch among L possible states where they assume the values r l and σ l , l = 0, .…”

Section: The Discretization In Each Regimementioning

confidence: 99%

“…The first one is relative to the fact that, in a two-regime economy, the option price in the low-volatility regime may present a worsening convergence rate when the volatilities in the two regimes are significantly different, as reported in Yuen and Yang (2009). The second one is relative to the fact that, as stated in Costabile et al (2014), it is not guaranteed that the transition probabilities are legitimate probabilities in all the regimes. Finally, the application of the forward shooting grid method of Hull and White (1993) to manage the path-dependent feature may produce the lack of convergence of the Yuen and Yang (2010) model, whenever the spanning function parameter h is not chosen as established in Forsyth et al (2002).…”

Section: Introductionmentioning

confidence: 99%

“…The financial literature on option pricing under regime-switching is quite large. If on one hand many papers have been proposed for standard option pricing by considering the regime risk not diversifiable (see Naik (1993), Di Masi et al (1994), Guo (2001), Mamon and Rodrigo (2005), and Elliott et al (2005), to name just a few), on the other hand many others have been developed by considering the regime risk as a diversifiable risk and using different pricing methodologies for option pricing (see Buffington and Elliott (2002), Yao et al (2006), Khaliq and Liu (2009), Liu et al (2006), Bollen (1998), Aingworth et al (2006), Liu (2010), Yuen and Yang (2009), and Costabile et al (2014), among others).…”

Section: Introductionmentioning

confidence: 99%

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A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model

Russo

2020

Risks

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This paper provides a discrete-time approach for evaluating financial and actuarial products characterized by path-dependent features in a regime-switching risk model. In each regime, a binomial discretization of the asset value is obtained by modifying the parameters used to generate the lattice in the highest-volatility regime, thus allowing a simultaneous asset description in all the regimes. The path-dependent feature is treated by computing representative values of the path-dependent function on a fixed number of effective trajectories reaching each lattice node. The prices of the analyzed products are calculated as the expected values of their payoffs registered over the lattice branches, invoking a quadratic interpolation technique if the regime changes, and capturing the switches among regimes by using a transition probability matrix. Some numerical applications are provided to support the model, which is also useful to accurately capture the market risk concerning path-dependent financial and actuarial instruments.

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Section: The Discretization In Each Regimementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model

Russo

2020

Risks

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“…Few studies have proposed a more realistic ROV model to encourage the decision making of the firm. To this fashion, Markov regime switching may be a useful approach to accommodate the time-varying aspect of parameters in ROV models [20][21][22]. Therefore, we suggest the valuation method with mean-reverting binomial lattice model under Markov regime switching.…”

Section: Introductionmentioning

confidence: 99%

R&D Project Valuation Considering Changes of Economic Environment: A Case of a Pharmaceutical R&D Project

Park

Shin

2018

Sustainability

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R&D project valuation is important for effective R&D portfolio management through decision making, related to the firm's R&D productivity, sustainable management. In particular, scholars have emphasized the necessities of capturing option value in R&D and developed methods of real option valuation. However, despite suggesting various real option models, there are few studies on simultaneously employing mean-reverting stochastic process and Markov regime switching to describe the evolution of cash flow and to reflect time-varying parameters resulting from changes of economic environment. Therefore, we suggest a mean-reverting binomial lattice model under Markov regime switching and apply it to evaluate clinical development with project cases of the pharmaceutical industry. This study finds that decision making can be different according to the regime condition, thus the suggested model can capture risks caused by the uncertainty of the economic environment, represented by regime switching. Further, this study simulates the model according to rate parameter from 0.00 to 1.00 and risk-free interest rates for regimes 1 and 2 from (r 1 = 4%, r 2 = 2%) to (r 1 = 7%, r 2 = 5%), and confirms the rigidity of the model. Therefore, in practice, the mean-reverting binomial lattice model under Markov regime switching proposed in this study for R&D project valuation contributes to assisting company R&D project managers make effective decision making considering current economic environment and future changes.

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“…Moreover, RS models represent a simple way to incorporate the stochastic volatility pattern revealed by financial variables (see for instance Li and Lin 2003). In RS models the parameters of the financial variables take on different values in different time periods according to a latent, unobservable process which generates switches among a finite set of regimes (see, among the others, Costabile et al 2013). RS were first introduced by Hamilton (1989), who studied an autoregressive RS process and estimated the model for the postwar US real GNP.…”

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confidence: 99%

A regime switching Ohlson model

Leccadito

Veltri

2014

Qual Quant

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This paper proposes a regime-switching version of the Ohlson model (Contemp Account Res 11:661-687, 1995). We assume that abnormal earnings and the other information variable follow a regime-switching dynamics, which represents a simple yet rigorous way to incorporate the stochastic volatility pattern revealed by financial variables. We derive closed form formulae for market values of equity and show that the resulting model is still tractable. In our empirical investigation we consider firms from the USA stock market during the period 1980-2011 and find that the regime-switching model improves upon the traditional Ohlson model in predicting market prices.

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A reduced lattice model for option pricing under regime-switching

Cited by 16 publications

References 28 publications

A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model

A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model

R&D Project Valuation Considering Changes of Economic Environment: A Case of a Pharmaceutical R&D Project

A regime switching Ohlson model

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