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3 edition of Portfolio allocation with heavy-tailed returns found in the catalog.

Portfolio allocation with heavy-tailed returns

Arnab Kumar Laha

Portfolio allocation with heavy-tailed returns

  • 178 Want to read
  • 36 Currently reading

Published by Indian Institute of Management in Ahmedabad .
Written in English

    Subjects:
  • Portfolio management -- India.,
  • Rate of return -- India.

  • Edition Notes

    StatementArnab Kumar Laha, Divyajyoti Bhowmick, Bharathy Subramaniam.
    SeriesWorking paper -- W.P. no. 2005-11-02
    ContributionsBhowmick, Divyajyoti., Indian Institute of Management, Ahmedabad.
    Classifications
    LC ClassificationsMicrofiche 2006/60186 (H)
    The Physical Object
    FormatMicroform
    Pagination1 v. (unpaged)
    ID Numbers
    Open LibraryOL16258741M
    LC Control Number2006561657

    Book chapter; Forecasting the Unconditional and Conditional Kurtosis of the Asset Returns Distribution. Ñíguez, T.M., Perote, J. and Rubia, A. Forecasting the Unconditional and Conditional Kurtosis of the Asset Returns Distribution. in: Molnar A, T. (ed.) Economic Forecasting Nova Cited by: 1. Shareable Link. Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. Centre for Finance and Financial Services. Flexible distribution functions, higher-order preferences and optimal portfolio allocation. Quantitative Finance. 19 (4), pp. doi Perote, J. and Rubia, A. Forecasting the Unconditional and Conditional Kurtosis of the Asset Returns Distribution. in: Molnar A, T. (ed.


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Portfolio allocation with heavy-tailed returns by Arnab Kumar Laha Download PDF EPUB FB2

Laha, A. & Bhowmick Divyajyoti & Subramaniam Bharathy, "Portfolio Allocation with Heavy-tailed returns," IIMA Working Papers WP, Indian Institute of Management Ahmedabad, Research and Publication : RePEc:iim:iimawp:wp In this sense, the analysis presented represents a general theory and a unifying framework to understand the parametric distributional approach to the portfolio choice theory.

Secondly, we show a simple classification of the portfolio choices considering the asymptotic behavior of. We discuss the question of portfolio selection when the returns of the assets under consideration are characterized by a heavy-tailed distribution.

As distributional assumption we consider the sub-Gaussian stable model and address the problems of estimation and portfolio : Toker Doganoglu, Stefan Mittnik, Stefan Mittnik, Svetlozar Rachev, Svetlozar Rachev.

As empirical studies show, the daily returns of a bond and its credit spread obey a stable law, exhibiting peaked, heavy tailed, and skewed distributions. This implies the application of stable Credit Value-at-Risk (CVaR) in order to obtain a more precise measure for a bond’s risk compared to.

IEOR E Quantitative Risk Management Fall c by Martin Haugh Asset Allocation and Risk Management These lecture notes provide an introduction to asset allocation and risk management.

We begin with a brief Note also that if we had assumed a heavy-tailed distribution for File Size: KB. () and u nder heavy-tailed di stributio ns.

The rest of thepaper is organized as fo llows: In Section 2, we pr esent the portfolio VaR and ES framework with an elliptic ran dom vector of. Open Library is an open, editable library catalog, building towards a web page for every book ever published.

Author of Portfolio allocation with heavy-tailed returns, Advances in. The impact of fat tailed returns on asset allocation. management approaches with heavy-tailed distributions tailored to take into account extreme comovements.

and portfolio allocation. Efficient frontier comprises investment portfolios that offer the highest expected return for a specific level of risk. Returns are dependent on the investment combinations that make up the portfolio.

Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type.

Downloadable. Using daily returns of the S&P stocks from towe perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses.

With more than stocks to choose from, our study seems to be the first application of extreme value Cited by: Portfolio Allocation with Heavy-tailed Returns, IIM-A Working Paper No.

(with Divyajyoti Bhoumick and Bharathy Subramaniam) A Likelihood Integrated Method for Exploratory Graphical Analysis of Change Point Problem with Direction Data, IIM-A.

where r (p) (t) is the DAXportfolio return at time t generated by the portfolio allocation at time (t-1), obtained by the optimal strategies obtained in solving the corresponding optimization. Asset Allocation: Balancing Financial Risk, Fifth Edition classes to a portfolio improves its risk-adjusted returns and how strategic asset allocation uses, asset allocation book.

The extensive data is updated with new chapters on the financial crisis and forecasting. This practical book focuses on. Portfolio Performance Attribution Final 1. Portfolio Performance Attribution Abstract In this paper, we provide further insight into the performance attribution by development of statistical models based on minimizing ETL performance risk with additional constraints on Asset Allocation (AA), Selection Effect (SE), and Total Expected Value Added by the portfolio managers (S).

Portfolio Selection in the Presence of Heavy-Tailed Asset Returns A Comparison of Gaussian and Non-Gaussian Portfolio Choice Models IFAC Proceedings Volumes, Vol. 34, No. 20Cited by: The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance.

Each individual volume in the series should present an - Selection from Handbook of Heavy Tailed Distributions in Finance [Book]. Start studying CAS Exam 9. Learn vocabulary, terms, and more with flashcards, games, and other study tools.

Non normal returns and optimal portfolio allocation. If the returns are more heavy tailed than a normal distribution would imply, it may be more appropriate to reduce the allocation to the risky portfolio than you would under the.

Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices.

These matrices are used as inputs to obtain out-of-sample Cited by: 2. portfolio allocation decisions made with a regional view. 1 Blake LeBaron. The Abram L. and Thelma Sachar Professor of International Economics International Business School, Brandeis University, Mailstop Waltham, MA [email protected] 2 Ritirupa Samanta.

Senior Associate. State Street Associates. State Street Bank and Trust Co. Conditional Value at Risk Asset Allocation A Copula Based Approach Hamed Naeini A Thesis in These risk measures quantify the uncertainty of portfolio returns around their expected CVaR is an ideally suited risk measure for handling heavy tailed distributions.

Moreover, the optimization of a portfolio based on CVaR is relatively easy. File Size: 3MB. Contrarian/Value investors don't buy into Modern Portfolio Theory as it depends on the Efficient Market Hypothesis and conflates fluctuations in shareprice with "risk".

This risk is only an opportunity to buy or sell assets at attractive prices inasmuch as it suits one's book. [15] Extensions.

Since MPT's introduction inmany attempts have been made to improve the model, especially by. Data sets with low kurtosis tend to have light tails, or lack of outliers. In computing kurtosis the formula used is μ4/σ4 where μ4 is Pearson’s fourth moment about the mean and sigma is the standard deviation.

The normal distribution (Gaussian) is found to have a kurtosis of three. The formula μ4/σ4 - 3 is the formula for excess. "Portfolio Allocation with Heavy Tailed Returns" (with Divyajyoti Bhoumick and Bharathy Subramaniam) () appeared in the journal Applied Financial Economics Letters, vol 4, issue 3, "Artificial Neural Network Models for Forecasting Stock Price Index in Bombay Stock Exchange" (with Neeraj Mohan, Pankaj Jha and Goutam Dutta)( The literature on portfolio selection and risk measurement has considerably advanced in recent years.

The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk by: 2.

The valuation of a portfolio, regardless of the method chosen, is always for the same purpose- to determine which portfolio yields the minimum risk or maximizes returns.

The main purpose of this paper is to determine which portfolios, constructed from stocks from KSE, would be most risky and which ones would be least risky.

It is sufficient to assume non-normal (unconditional) returns to explain the difference between monthly and daily estimates of annualized volatility. If you assume conditionally normal returns with stochastic volatility you have GARCH models; if the conditional returns are heavy-tailed, you have Levy process.

Portfolio modeling with heavy tailed random vectors by Mark M. Meerschaert, Hans-Peter Scheffler - Handbook of Heavy-Tailed Distributions in Finance, Since the work of Mandelbrot in the ’s there has accumulated a great deal of empirical evidence for heavy tailed models in finance.

If you had a portfolio of 30 equally weighted assets, and these assets would give you income streams 60% correlated with each other and each stream had a Sharpe ratio — the expected excess rate of return divided by its standard deviation— ofthen the portfolio Sharpe ratio would be: Sharpe_c = n*/(n + 2*n(n − 1)/2 *)^ = The Theory and Practice of Investment Management: Asset Allocation, Valuation, Portfolio Construction, and Strategies by Frank J.

Fabozzi and Harry M. Markowitz | Apr 5, out of 5 stars 5. Quantifying the systemic risk and fragility of financial systems is of vital importance in analyzing market efficiency, deciding on portfolio allocation, and containing financial contagions.

At a high level, financial systems may be represented as weighted graphs that characterize the complex web of interacting agents and information flow (for example, debt, stock returns, and shareholder Cited by: Modern Portfolio Management Using Capital Asset Pricing And Fama-French Three Factor Model [EPAT PROJECT] Consequently, asset returns are said to follow a leptokurtic distribution or heavy-tailed distribution.

PORTFOLIOALPHA= PORTFOLIOVOLATILITY= MOMENTUM= PORTFOLIO RETURNS. In this paper we show how to formulate and solve robust portfolio selection problems.

The objective of these robust formulations is to systematically combat the sensitivity of the optimal portfolio to statistical and modeling errors in the estimates of the relevant market by: Maximize the Sharpe Ratio and Minimize a VaR1 Robert B.

Durand2 Hedieh Jafarpour3,4 Claudia Klüppelberg5 Ross Maller6 portfolio by taking an allocation, that is, a linear combination with coefficients given by a vec- the probabilities of efficient portfolio returns lower than q are shown for various values Size: KB.

Portfolio Allocation among following asset classes (distributions are discrete): • T-bills • government bonds • corporate bonds • six size/book-to-market Fama-French equity portfolios Notation • w portfolio weights • R vector of asset returns • R(Crash of 87) asset returns during the crash • R(9/11) asset returns just after.

and finds an allocation such that bond income is sufficient to c over the • Immunization: the portfolio is constructed by matching the present values and interest rate sensitivities of the assets and liabilities. Allocation that • Financial returns are typically heavy-tailed. The third chapter studies intertemporal portfolio allocation under a time-varying covariance matrix of stock returns using a dynamic conditional correlation model.

The fourth chapter is on portfolio risk management using a copula-based model that accounts for the distributional characteristics and tail dependence of stock returns. Endogenous extreme events and the dual role of prices Procyclical leverage and endogenous risk Exchange rate determination and inter-market order flow effects Regime switches in the volatility and correlation of financial institutions Fat tails, VaR and subadditivity Liquidity determination in an order driven market Systemic risk arising from computer based trading and connections to the.

especially for assets with returns that are heavy-tailed. Second, minimizing CVaR usually results in solving a convex programming problem, such as a linear programming problem, which allows the decision maker to deal with a large scale portfolio problem e–ciently (Rock File Size: KB.

Abstract. Excluding the assumption of normality in return distributions, a general reward-risk ratio suitable to compare portfolio returns with respect to a benchmark must includes asymmetrical information on both “good” volatility (above the benchmark) and “bad” volatility (below the Cited by: 6.

Quantifying the systemic risk and fragility of financial systems is of vital importance in analyzing market efficiency, deciding on portfolio allocation, and containing financial contagions. At a high level, financial systems may be represented as weighted graphs that characterize the complex web of interacting agents and information flow (for Cited by: Quantitative Risk Management: Concepts, Techniques, and Tools.

Company: Princeton University Press Year Of Publication: e–cient portfolio frontier for these investors is derived from minimizing min xj ¾2 p = Xm i=1 Xm j=1 xixj¾ij; (2) with ¾ij standing for the covariance between returns and ¾2 j for the variance.

If there exits a risk-free asset in the economy the e–cient portfolio frontier is determined by a straight line of this form E[Rj]¡Rf = flj(E.