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-Up-to-date with cutting edge topics -Suitable for professional
quants and as library reference for students of finance and
financial mathematics
High-Performance Computing (HPC) delivers higher computational
performance to solve problems in science, engineering and finance.
There are various HPC resources available for different needs,
ranging from cloud computing- that can be used without much
expertise and expense - to more tailored hardware, such as
Field-Programmable Gate Arrays (FPGAs) or D-Wave's quantum computer
systems. High-Performance Computing in Finance is the first book
that provides a state-of-the-art introduction to HPC for finance,
capturing both academically and practically relevant problems.
The First Collection That Covers This Field at the Dynamic
Strategic and One-Period Tactical Levels Addressing the imbalance
between research and practice, Quantitative Fund Management
presents leading-edge theory and methods, along with their
application in practical problems encountered in the fund
management industry. A Current Snapshot of State-of-the-Art
Applications of Dynamic Stochastic Optimization Techniques to
Long-Term Financial Planning The first part of the book initially
looks at how the quantitative techniques of the equity industry are
shifting from basic Markowitz mean-variance portfolio optimization
to risk management and trading applications. This section also
explores novel aspects of lifetime individual consumption
investment problems, fixed-mix portfolio rebalancing allocation
strategies, debt management for funding mortgages and national
debt, and guaranteed return fund construction. Up-to-Date Overview
of Tactical Financial Planning and Risk Management The second
section covers nontrivial computational approaches to tactical fund
management. This part focuses on portfolio construction and risk
management at the individual security or fund manager level over
the period up to the next portfolio rebalance. It discusses
non-Gaussian returns, new risk-return tradeoffs, and the robustness
of benchmarks and portfolio decisions. The Future Use of
Quantitative Techniques in Fund Management With contributions from
well-known academics and practitioners, this volume will
undoubtedly foster the recognition and wider acceptance of
stochastic optimization techniques in financial practice.
Quantitative Finance: An Object-Oriented Approach in C++ provides
readers with a foundation in the key methods and models of
quantitative finance. Keeping the material as self-contained as
possible, the author introduces computational finance with a focus
on practical implementation in C++. Through an approach based on
C++ classes and templates, the text highlights the basic principles
common to various methods and models while the algorithmic
implementation guides readers to a more thorough, hands-on
understanding. By moving beyond a purely theoretical treatment to
the actual implementation of the models using C++, readers greatly
enhance their career opportunities in the field. The book also
helps readers implement models in a trading or research
environment. It presents recipes and extensible code building
blocks for some of the most widespread methods in risk management
and option pricing. Web ResourceThe author's website provides fully
functional C++ code, including additional C++ source files and
examples. Although the code is used to illustrate concepts (not as
a finished software product), it nevertheless compiles, runs, and
deals with full, rather than toy, problems. The website also
includes a suite of practical exercises for each chapter covering a
range of difficulty levels and problem complexity.
The First Collection That Covers This Field at the Dynamic
Strategic and One-Period Tactical Levels Addressing the imbalance
between research and practice, Quantitative Fund Management
presents leading-edge theory and methods, along with their
application in practical problems encountered in the fund
management industry. A Current Snapshot of State-of-the-Art
Applications of Dynamic Stochastic Optimization Techniques to
Long-Term Financial Planning The first part of the book initially
looks at how the quantitative techniques of the equity industry are
shifting from basic Markowitz mean-variance portfolio optimization
to risk management and trading applications. This section also
explores novel aspects of lifetime individual consumption
investment problems, fixed-mix portfolio rebalancing allocation
strategies, debt management for funding mortgages and national
debt, and guaranteed return fund construction. Up-to-Date Overview
of Tactical Financial Planning and Risk Management The second
section covers nontrivial computational approaches to tactical fund
management. This part focuses on portfolio construction and risk
management at the individual security or fund manager level over
the period up to the next portfolio rebalance. It discusses
non-Gaussian returns, new risk-return tradeoffs, and the robustness
of benchmarks and portfolio decisions. The Future Use of
Quantitative Techniques in Fund Management With contributions from
well-known academics and practitioners, this volume will
undoubtedly foster the recognition and wider acceptance of
stochastic optimization techniques in financial practice.
High-Performance Computing (HPC) delivers higher computational
performance to solve problems in science, engineering and finance.
There are various HPC resources available for different needs,
ranging from cloud computing- that can be used without much
expertise and expense - to more tailored hardware, such as
Field-Programmable Gate Arrays (FPGAs) or D-Wave's quantum computer
systems. High-Performance Computing in Finance is the first book
that provides a state-of-the-art introduction to HPC for finance,
capturing both academically and practically relevant problems.
The use of derivative products in risk management has spread from
commodities, stocks and fixed income items, to such virtual
commodities as energy, weather and bandwidth. All this can give
rise to so-called volatility and there has been a consequent
development in formal risk management techniques to cover all types
of risk: market, credit, liquidity, etc. One of these techniques,
Value at Risk, was developed specifically to help manage market
risk over short periods. Its success led, somewhat controversially,
to its take up and extension to credit risk over longer
time-scales. This extension, ultimately not successful, led to the
collapse of a number of institutions. The present book, which was
originally published in 2002, by some of the leading figures in
risk management, examines the complex issues that concern the
stability of the global financial system by presenting a mix of
theory and practice.
The theory of Value at Risk (VaR), which quantifies the probability of large losses in financial transactions, won the Nobel Prize in economics for Robert Merton. As trading systems have become more complex, however, the dangers of very large losses have become more acute. The near collapse of the hedge fund Long-Term Capital Management, based on the VaR theory, is perhaps the most spectacular example: it was not stable against large and sudden fluctuations in the financial markets. This collection of papers by leading researchers addresses the weaknesses of VaR and how it might be possible to circumvent them. A crucial question is to establish what is a good measure of risk, and the further developments of VaR are considered in this light.
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