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Zero-Cost Collar Strategy For Chilean Exporters: Black-Scholes Valuation Vs Monte Carlo Simulations, Estrategia Collar Costo Cero Para Exportadores Chilenos. Valuacion De Black-Scholes Vs Simulaciones De Montecarlo

Author

Listed:
  • Eduardo Sandoval
  • Paula Urrutia

Abstract

Monte Carlo Simulation (MCS) is an important technique used in finance to value European options in general and hedging strategies of exchange rate risk in particular. However, Hull (2012) indicates that this technique demands much computing time to achieve a high level of precision. In spite of the above, this article shows that the standard errors associated with MCS can be significantly reduced by increasing the number of random walks. Additionally, it is shown that the valuation of zero-cost collar strategy followed by a Chilean exporter, who seeks monthly coverage against foreign exchange risk, converges at the same price either using the Black-Scholes model or MCS when the number of random walks increases through simulations. Finally, the effectiveness of zero-cost collar strategy is evaluated. This strategy shows clear economic benefits associated of being implemented in periods of appreciation of the Chilean peso against the U.S. dollar.

Suggested Citation

  • Eduardo Sandoval & Paula Urrutia, 2014. "Zero-Cost Collar Strategy For Chilean Exporters: Black-Scholes Valuation Vs Monte Carlo Simulations, Estrategia Collar Costo Cero Para Exportadores Chilenos. Valuacion De Black-Scholes Vs Simulaciones," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 7(5), pages 25-40.
    • Handle: RePEc:ibf:riafin:v:7:y:2014:i:5:p:25-40
    as

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    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Carriere, Jacques F., 1996. "Valuation of the early-exercise price for options using simulations and nonparametric regression," Insurance: Mathematics and Economics, Elsevier, vol. 19(1), pages 19-30, December.
    3. Marta Casas Monsegny & Edilberto Cepeda, 2008. "Modelos ARCH, GARCH y EGARCH: aplicaciones a series financieras," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, July.
    4. repec:bla:jfinan:v:44:y:1989:i:2:p:375-92 is not listed on IDEAS
    5. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    6. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Zero-cost Collar Strategy; Black-Scholes Valuation; Monte Carlo Simulations;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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