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1

Pascucci, Andrea. PDE and Martingale Methods in Option Pricing. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1781-8.

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2

Chorro, Christophe, Dominique Guégan, and Florian Ielpo. A Time Series Approach to Option Pricing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45037-6.

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3

Page, H. A practical approach to option pricing theory. Dublin: University College Dublin, 1994.

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4

Risk-adjusted lending conditions: An option pricing approach. Chichester: John Wiley & Sons, 2002.

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Rosenberger, Werner. Risk-adjusted lending conditions: An option pricing approach. Chichester: John Wiley, 2003.

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6

Lee, Jaewoo. Insurance value of international reserves: An option pricing approach. [Washington D.C.]: International Monetary Fund, Research Dept., 2004.

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7

Baranzini, Andrea. Uncertainty and global warming: An option-pricing approach to policy. Washington, D.C: World Bank, Latin America and the Caribbean, Country Dept. I, Country Operations Division, 1995.

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8

Katz, Jeffrey Owen. Advanced option pricing models: An empirical approach to valuing options. New York: McGraw-Hill, 2005.

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9

Claessens, Stijn. An option-pricing approach to secondary market debt: Applied to Mexico. Washington, DC: Data and International Finance Division, International Economics Dept. and the Country Operations Division, Latin America and the Caribbean Country Dept. II, World Bank, 1990.

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10

Estache, Antonio. Evaluating the minimum asset tax on corporations: An option pricing approach. London: Centre for Economic Policy Research, 1992.

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11

Onimus, Jil Caroline. Assessing the Economic Value of Venture Capital Contracts: An Option Pricing Approach. Wiesbaden: Gabler Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2011.

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12

Pde And Martingale Methods In Option Pricing. Springer, 2011.

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13

Pascucci, Andrea. PDE and Martingale Methods in Option Pricing. Springer, 2014.

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14

Pascucci, Andrea. PDE and Martingale Methods in Option Pricing. Springer, 2011.

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15

Back, Kerry E. Forwards, Futures, and More Option Pricing. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0017.

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Forward measures are defined. Forward and futures contracts are explained. The spot‐forward parity formula is derived. A forward price is a martingale under the forward measure. A futures price is a martingale under a risk neutral probability. Forward prices equal futures prices when interest rates are nonrandom. The expectations hypothesis is explained. The option pricing formulas of Margabe (exchange options), Black (options on forwards), and Merton (random interest rates) are derived. Implied volatilities and local volatility models are explained. Heston’s stochastic volatility model is derived.
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16

Jarrow, Robert A. Continuous-Time Asset Pricing Theory: A Martingale-Based Approach. Springer, 2019.

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17

Jarrow, Robert A. Continuous-Time Asset Pricing Theory: A Martingale-Based Approach. Springer, 2018.

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18

Perrakis, Stylianos. Stochastic Dominance Option Pricing: An Alternative Approach to Option Market Research. Palgrave Macmillan, 2019.

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19

Rosenberger, Werner. Risk-Adjusted Lending Conditions: An Option Pricing Approach. Wiley & Sons, Incorporated, John, 2003.

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20

Rosenberger, Werner. Risk-Adjusted Lending Conditions: An Option Pricing Approach. Wiley & Sons, Incorporated, John, 2010.

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21

Baranzini, Andrea, Marc Chesney, and Jacques Morisset. Uncertainty and Global Warming: An Option-Pricing Approach to Policy. The World Bank, 1999. http://dx.doi.org/10.1596/1813-9450-1417.

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22

Rosenberger, Werner. Risk-adjusted Lending Conditions: An Option Pricing Approach (The Wiley Finance Series). Wiley, 2003.

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23

Björk, Tomas. Arbitrage Theory in Continuous Time. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198851615.001.0001.

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The fourth edition of this textbook on pricing and hedging of financial derivatives, now also including dynamic equilibrium theory, continues to combine sound mathematical principles with economic applications. Concentrating on the probabilistic theory of continuous time arbitrage pricing of financial derivatives, including stochastic optimal control theory and optimal stopping theory, the book is designed for graduate students in economics and mathematics, and combines the necessary mathematical background with a solid economic focus. It includes a solved example for every new technique presented, contains numerous exercises, and suggests further reading in each chapter. All concepts and ideas are discussed, not only from a mathematics point of view, but the mathematical theory is also always supplemented with lots of intuitive economic arguments. In the substantially extended fourth edition Tomas Björk has added completely new chapters on incomplete markets, treating such topics as the Esscher transform, the minimal martingale measure, f-divergences, optimal investment theory for incomplete markets, and good deal bounds. There is also an entirely new part of the book presenting dynamic equilibrium theory. This includes several chapters on unit net supply endowments models, and the Cox–Ingersoll–Ross equilibrium factor model (including the CIR equilibrium interest rate model). Providing two full treatments of arbitrage theory—the classical delta hedging approach and the modern martingale approach—the book is written in such a way that these approaches can be studied independently of each other, thus providing the less mathematically oriented reader with a self-contained introduction to arbitrage theory and equilibrium theory, while at the same time allowing the more advanced student to see the full theory in action.
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24

Lux, Thomas, and Mawuli Segnon. Multifractal Models in Finance. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.8.

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This chapter provides an overview over the recently developed so-called multifractal (MF) approach for modeling and forecasting volatility. For analysts and policy makers, volatility is a key variable for understanding market fluctuations. Analysts need accurate forecasts of volatility for tasks such as risk management, as well as option and futures pricing. In addition, asset market volatility plays an important role in monetary policy. The chapter, then, outlines the genesis of the multifractal approach from similar models of turbulent flows in statistical physics and provides details about different specifications of multifractal time series models in finance, available methods for their estimation, and the current state of their empirical applications.
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