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Author: Francesco Paolo Natale
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper proposes a method to overcome the classical drawbacks of the Monte Carlo methods for the asset allocation, namely resampling, deeply dependent upon the multinormal assumption. The proposed approach allows to set a barrier against joint extreme negative returns (tail-dependence) and extreme (negative) returns (univariate tail risk) not included in the multivariate normal distribution. The dangerous tail-dependence between asset returns is considered by using a copula based approach instead of the multinormal Monte Carlo simulation. Then the proposed model has been applied on a sample of eleven euro-denominated asset classes with historical inputs and the consequent asset weights have been tested on multivariate Student's t returns and on a set of out-of-the sample real returns. The results of this model provide evidence of a barrier against extreme negative returns occurring simultaneously. The proposed model is distribution-free and therefore it does not involve any a priori decision on the marginal distributions for asset returns.
Author: Francesco Paolo Natale
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper proposes a method to overcome the classical drawbacks of the Monte Carlo methods for the asset allocation, namely resampling, deeply dependent upon the multinormal assumption. The proposed approach allows to set a barrier against joint extreme negative returns (tail-dependence) and extreme (negative) returns (univariate tail risk) not included in the multivariate normal distribution. The dangerous tail-dependence between asset returns is considered by using a copula based approach instead of the multinormal Monte Carlo simulation. Then the proposed model has been applied on a sample of eleven euro-denominated asset classes with historical inputs and the consequent asset weights have been tested on multivariate Student's t returns and on a set of out-of-the sample real returns. The results of this model provide evidence of a barrier against extreme negative returns occurring simultaneously. The proposed model is distribution-free and therefore it does not involve any a priori decision on the marginal distributions for asset returns.
Author: Takuo Higashide
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
In the world of investment, the subject of building a portfolio concerning tail risk is still one of the frequently discussed subjects and unquestionably vital for investors. This paper seeks to examine how the risk measures, lower tail-dependence based on the copulas approach and Conditional Value-at-Risk (CVaR), affect the portfolio strategies and play important roles for tail risk management and diversify the portfolio. By using these two risk measures mentioned above, two different types of risk-based portfolios are proposed that consider for the tail risks: 1) Minimum-lower tail-dependence portfolio (RMTP) and 2) Risk Parity Portfolio based on Conditional Value-at-Risk (CRPP). The simulation results showed how those two risk-based portfolios, RMTP and CRPP, work effectively in multi-asset allocation framework with 6 assets: stocks and sovereign bonds of Japan, United States and Germany, based on the monthly rebalance rule, using 2004-2018 sample period. One of the key findings were that both RMTP and CRPP strategies delivered better performances compared with the traditional portfolio strategies in terms of sharp ratio: 1) RMTP yielded 0.92 and 2) CRPP yielded 0.99 (by adding an appropriate risk reduction to this portfolio, the sharp ratio went up to 1.76). In addition, both of these two strategies also worked effectively in terms of the average of maximum monthly drawdown related to the effect of the tail risk: 1) RMTP by 1.80% and 2) CRPP by 1.74% (by adding an appropriate risk reduction to this portfolio, maximum drawdown decreased to 0.78%). Furthermore, this paper also studies an enhancement strategy based on Risk Parity Portfolios (RPP) focusing on and using co-integration relationship (co-integration approach). According to the simulation result, this proposed enhancement strategy has a potential to yield roughly 4.5% return. Finally, this paper presents the explicit derivation of lower tail-dependence and co-integration approach.
Author:
Publisher: Rmetrics
ISBN:
Category :
Languages : en
Pages : 455
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Book Description
Author: Fangda Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
Inspired by the recent developments in risk sharing problems for the Value-at-Risk (VaR), the Expected Shortfall (ES), or the Range-Value-at-Risk (RVaR), we study the optimization of risk sharing for general tail risk measures. Explicit formulas of the inf-convolution and Pareto optimal allocations are obtained in the case of a mixed collection of left and right VaRs, and in that of a VaR and another tail risk measure. The inf-convolution of tail risk measures is shown to be a tail risk measure with an aggregated tail parameter, a phenomenon very similar to the cases of VaR , ES and RVaR. The technical conclusions are quite general without assuming any form of convexity of the tail risk measures. Moreover, we find, via several results, that the roles of left and right VaRs are generally asymmetric in the optimization problems. Our analysis generalizes in several directions the recent work on quantile-based risk sharing.
Author: Bernhard Pfaff
Publisher: John Wiley & Sons
ISBN: 1119119685
Category : Mathematics
Languages : en
Pages : 448
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Book Description
Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.
Author: Bernd Scherer
Publisher: Springer Science & Business Media
ISBN: 038727586X
Category : Business & Economics
Languages : en
Pages : 422
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Book Description
In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.
Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
ISBN: 1786305445
Category : Business & Economics
Languages : en
Pages : 460
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Book Description
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Business & Economics
Languages : en
Pages : 218
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Book Description
Who is Harry Markowitz An American economist named Harry Max Markowitz was awarded the John von Neumann Theory Prize in 1989 and the Nobel Memorial Prize in Economic Sciences in 1990. He was also a recipient of both of these honors. How you will benefit (I) Insights about the following: Chapter 1: Harry Markowitz Chapter 2: Robert C. Merton Chapter 3: Capital asset pricing model Chapter 4: Merton Miller Chapter 5: William F. Sharpe Chapter 6: Modern portfolio theory Chapter 7: SIMSCRIPT Chapter 8: Roger G. Ibbotson Chapter 9: Diversification (finance) Chapter 10: Leonid Hurwicz Chapter 11: Post-modern portfolio theory Chapter 12: Finance Chapter 13: Portfolio manager Chapter 14: Andrew Lo Chapter 15: Maslowian portfolio theory Chapter 16: Portfolio optimization Chapter 17: Quantitative analysis (finance) Chapter 18: Downside risk Chapter 19: Mathematical finance Chapter 20: Index Fund Advisors Chapter 21: Philippe De Brouwer Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information about Harry Markowitz.
Author: Thierry Roncalli
Publisher: CRC Press
ISBN: 1482207168
Category : Business & Economics
Languages : en
Pages : 430
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Book Description
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of investment after the global fina
Author: Dr. Parveen Chauhan
Publisher: Xoffencerpublication
ISBN: 811953428X
Category : Business & Economics
Languages : en
Pages : 303
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Book Description
The concept of fuzzy logic refers to a specific subset of many-valued logic. In this line of reasoning, the truth value of a variable can be any real integer, including any fraction that is between 0 and 1. This applies to all fractions as well. It achieves this by regulating the concept of partial truth, in which the truth value may switch between being entirely true and entirely false at any given moment. This objective may be accomplished by making use of the tool for managing concepts. In contrast, the truth values of variables in Boolean logic can never be anything other than the integer values 0 or 1, as there are only two alternatives that even have a remote chance of occurring. This is because there are only two options that are even remotely imaginable. It is common practice to consider the fuzzy set theory, which was created in 1965 by the Iranian-Azerbaijani mathematician Lotfi Zadeh, to be the basis for fuzzy logic. However, since the 1920s, scholars have been investigating fuzzy logic, which was also known as infinite-valued logic at the time. Most notably, Lukasiewicz and Tarski were the researchers that began this line of inquiry. This particular investigation didn't wrap up until the 1960s, but it began in the 1920s. The idea of fuzzy logic is based on the fact that decision-makers frequently rely on hazy and non-numerical information. In other words, this is the origin of fuzzy logic. The mathematical methods of fuzzy modeling and fuzzy set creation, both of which are used to describe ambiguous and imprecise information, are where the name "fuzzy" first appeared. These models are capable of recognizing, representing, manipulating, understanding, and using facts and information that are fundamentally hazy and ambiguous in nature. Fuzzy logic has been effectively applied in a variety of applications, from control theory to artificial intelligence. Conventional patterns of thinking can only ever lead to conclusions that are either correct or incorrect. However, there are other statements that may elicit a range of responses, such as the answers you could get if you asked a group of individuals to name a color. One that invites people to name a meal is another 1 | P a ge illustration of this kind of proposal. In situations like this, it is the application of reasoning based on incomplete or inaccurate information that leads to the finding of the truth. This argument entails plotting the sampled responses on a spectrum. Although degrees of truth and probabilities both range from 0 to 1, fuzzy logic employs degrees of truth as a mathematical model of ambiguity whereas probability is a mathematical model of ignorance, despite the fact that they may initially appear to be the same. Although they could at first glance appear to be the same because both probability and degrees of truth range from 0 to 1, this is only because they do.
