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JournalISSN: 1465-1211

Journal of Risk 

Infopro Digital
About: Journal of Risk is an academic journal published by Infopro Digital. The journal publishes majorly in the area(s): Value at risk & Volatility (finance). It has an ISSN identifier of 1465-1211. Over the lifetime, 531 publications have been published receiving 17192 citations.


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Journal ArticleDOI
TL;DR: In this paper, a new approach to optimize or hedging a portfolio of financial instruments to reduce risk is presented and tested on applications, which focuses on minimizing Conditional Value-at-Risk (CVaR) rather than minimizing Value at Risk (VaR), but portfolios with low CVaR necessarily have low VaR as well.
Abstract: A new approach to optimizing or hedging a portfolio of nancial instruments to reduce risk is presented and tested on applications. It focuses on minimizing Conditional Value-at-Risk (CVaR) rather than minimizing Value-at-Risk (VaR), but portfolios with low CVaR necessarily have low VaR as well. CVaR, also called Mean Excess Loss, Mean Shortfall, or Tail VaR, is anyway considered to be a more consistent measure of risk than VaR. Central to the new approach is a technique for portfolio optimization which calculates VaR and optimizes CVaR simultaneously. This technique is suitable for use by investment companies, brokerage rms, mutual funds, and any business that evaluates risks. It can be combined with analytical or scenario-based methods to optimize portfolios with large numbers of instruments, in which case the calculations often come down to linear programming or nonsmooth programming. The methodology can be applied also to the optimization of percentiles in contexts outside of nance.

5,622 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider the execution of portfolio transactions with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact, and they explicitly construct the efficient frontier in the space of time-dependent liquidation strategies, which have minimum expected cost for a given level of uncertainty.
Abstract: We consider the execution of portfolio transactions with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. For a simple linear cost model, we explicitly construct the efficient frontier in the space of time-dependent liquidation strategies, which have minimum expected cost for a given level of uncertainty. We may then select optimal strategies either by minimizing a quadratic utility function, or by minimizing Value at Risk. The latter choice leads to the concept of Liquidity-adjusted VAR, or L-VaR, that explicitly considers the best tradeoff between volatility risk and liquidation costs. ∗We thank Andrew Alford, Alix Baudin, Mark Carhart, Ray Iwanowski, and Giorgio De Santis (Goldman Sachs Asset Management), Robert Ferstenberg (ITG), Michael Weber (Merrill Lynch), Andrew Lo (Sloan School, MIT), and George Constaninides (Graduate School of Business, University of Chicago) for helpful conversations. This paper was begun while the first author was at the University of Chicago, and the second author was first at Morgan Stanley Dean Witter and then at Goldman Sachs Asset Management. †University of Toronto, Departments of Mathematics and Computer Science; almgren@math.toronto.edu ‡ICor Brokerage and Courant Institute of Mathematical Sciences; Neil.Chriss@ICorBroker.com

1,258 citations

Journal ArticleDOI
TL;DR: In this article, a new approach for optimization of Conditional Value-at-Risk (CVaR) was suggested and tested with several applications, and the approach can be used for maximizing expected returns under CVaR constraints.
Abstract: Recently, a new approach for optimization of Conditional Value-at-Risk (CVaR) was suggested and tested with several applications. For continuous distributions, CVaR is defined as the expected loss exceeding Value-at Risk (VaR). However, generally, CVaR is the weighted average of VaR and losses exceeding VaR. Central to the approach is an optimization technique for calculating VaR and optimizing CVaR simultaneously. This paper extends this approach to the optimization problems with CVaR constraints. In particular, the approach can be used for maximizing expected returns under CVaR constraints. Multiple CVaR constraints with various confidence levels can be used to shape the profit/loss distribution. A case study for the portfolio of S&P 100 stocks is performed to demonstrate how the new optimization techniques can be implemented.

729 citations

Journal ArticleDOI
Michel Denault1
TL;DR: In this paper, the Aumann-Shapley value is used as a coherent and practical approach to financial risk allocation, which is axiomatic, in the sense that they first argue for the nec- essary properties of an allocation principle, and then consider principles that fulfill the properties.
Abstract: The allocation problem stems from the diversification effect observed in risk measurements of financial portfolios: the sum of the "risks" of many portfolios is larger than the "risk" of the sum of the portfolios. The allocation problem is to apportion this diversification advantage to the portfolios in a fair manner, yielding, for each portfolio, a risk appraisal that accounts for diversification. Our approach is axiomatic, in the sense that we first argue for the nec- essary properties of an allocation principle, and then consider principles that fulfill the properties. Important results from the area of game theory find a direct application. Our main result is that the Aumann-Shapley value is both a coherent and practical approach to financial risk allocation.

437 citations

Journal ArticleDOI
TL;DR: In this paper, a procedure for using a GARCH or exponentially weighted moving average model in conjunction with historical simulation when computing value at risk is proposed, which involves adjusting historical data on each market variable to reflect the difference between the historical volatility of the market variable and its current volatility.
Abstract: This paper proposes a procedure for using a GARCH or exponentially weighted moving average model in conjunction with historical simulation when computing value at risk. It involves adjusting historical data on each market variable to reflect the difference between the historical volatility of the market variable and its current volatility. We compare the approach using nine years of daily data on 12 exchange rates and 5 stock indices with the historical simulation approach and show that it is a substantial improvement.

328 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202314
202220
20217
202017
201919
201822