Tim Siu-tang Leung – författare

Futures play an integral role in the financial markets. Tens of millions of contracts are traded on futures exchanges around the globe every day. In recent years, futures have been incorporated into a wide array of financial securities and have become the driving force behind their price dynamics. Managed futures portfolios and commodity trading advisors (CTAs), with hundreds of billions under management, are major parts of the hedge fund industry.This book presents a unique stochastic control approach to dynamic futures trading. Multiple stochastic models are designed to capture the salient features of various market regimes and dynamics. They are useful for pricing futures contracts and building futures portfolios. The authors analyze the mathematical problems associated with futures trading problems in different market environments. A series of numerical examples are presented to illustrate the optimal trading strategies. In addition, analytic formulas and numerical methods are provided for fast implementation. The book is useful for practitioners interested in futures trading as well as graduate students and researchers in Quantitative Finance.

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Employee stock options (ESOs) are an integral component of compensation in the US. In fact, almost all S&P 500 companies grant options to their top executives, and the total value accounts for almost half of the total pay for their CEOs. In view of the extensive use and significant cost of ESOs to firms, the Financial Accounting Standards Board (FASB) has mandated expensing ESOs since 2004. This gives rise to the need to create a reasonable valuation method for these options for most firms that grant ESOs to their employees. The valuation of ESOs involves a number of challenging issues, and is thus an important active research area in Accounting, Corporate Finance, and Financial Mathematics.In this exciting book, the author discusses the practical and challenging problems surrounding ESOs from a financial mathematician's perspective. This book provides a systematic overview of the contractual features of ESOs and thoughtful discussions of different valuation approaches, with emphasis on three major aspects: (i) hedging strategies; (ii) exercise timing; and (iii) valuation methodologies. In addition to addressing each of these categories, this book also highlights their connections and combined effects of the cost of ESOs to firms, as well as examines the implications to modeling and valuation approaches. The book features a unique approach that combines stochastic modeling and control techniques with option pricing theory, and provides formulas and numerical schemes for fast implementation and clear illustration.