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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis FREE CHAPTER 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Hedging in the presence of transaction costs


As we shown earlier, increasing the number of portfolio adjustments leads to a decrease in the volatility of the hedging cost. As Δt approaches 0, the cost of hedging approximates the option price derived from the BS formula. Until now, we have disregarded the transaction costs, but here, we remove this assumption and analyze the effects of transaction costs on option hedging. As rebalancing becomes more frequent, transaction costs increase the cost of hedging, but at the same time, shorter rebalancing periods reduce the volatility of the hedging cost. Hence, it is worth examining this trade-off in more detail, and based on this, defining the optimal rebalancing strategy. An absolute (fixed for each transaction) or a relative (proportional to the transaction size) transaction cost can be added to the code by modifying the parameters when we define the function:

cost_simulation = function(S0, mu, sigma, rf, K, Time, dt, periods, cost_per_trade...
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