Empirical Asset Return Distributions: Is Chaos the Culprit?

2003 ◽  
Author(s):  
Cal B. Muckley
Keyword(s):  
Asset Return ◽  
Return Distributions

Expansion methods applied to asset return distributions

2007 ◽  
Vol 10 (2) ◽  
pp. 3-24 ◽  
Author(s):  
Kohei Marumo ◽  
Rodney Wolff
Keyword(s):  
Asset Return ◽  
Return Distributions

Country risk and the estimation of asset return distributions

2007 ◽  
Vol 7 (3) ◽  
pp. 261-265 ◽  
Author(s):  
Robert Brooks ◽  
Xibin Zhang ◽  
Emawtee Bissoondoyal Bheenick
Keyword(s):  
Country Risk ◽  
Asset Return ◽  
Return Distributions

scholarly journals ON OPTIMAL STRATEGIES FOR UTILITY MAXIMIZERS IN THE ARBITRAGE PRICING MODEL

2016 ◽  
Vol 19 (07) ◽  
pp. 1650047 ◽  
Author(s):  
MIKLÓS RÁSONYI
Keyword(s):  
Expected Utility ◽  
Optimal Strategies ◽  
Asset Returns ◽  
Optimal Investment ◽  
Pricing Model ◽  
Arbitrage Pricing ◽  
Asset Return ◽  
Return Distributions ◽  
Restrictive Hypothesis

We consider a popular model of microeconomics with countably many assets: the Arbitrage Pricing Model. We study the problem of optimal investment under an expected utility criterion and look for conditions ensuring the existence of optimal strategies. Previous results required a certain restrictive hypothesis on the tails of asset return distributions. Using a different method, we manage to remove this hypothesis, at the price of stronger assumptions on the moments of asset returns.

Estimating Flexible, Fat-Tailed Asset Return Distributions

2012 ◽  
Author(s):  
Craig A. Friedman ◽  
Yangyong Zhang ◽  
Jinggang Huang
Keyword(s):  
Asset Return ◽  
Return Distributions

Asset Return Distributions and the Investment Horizon

CFA Digest ◽  
2004 ◽  
Vol 34 (4) ◽  
pp. 58-59
Author(s):  
Johann U. de Villiers
Keyword(s):  
Investment Horizon ◽  
Asset Return ◽  
Return Distributions

scholarly journals On Partial Stochastic Comparisons Based on Tail Values at Risk

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1181
Author(s):  
Alfonso J. Bello ◽  
Julio Mulero ◽  
Miguel A. Sordo ◽  
Alfonso Suárez-Llorens
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Real Data ◽  
Stochastic Orders ◽  
Stochastic Comparisons ◽  
Increasing Convex Order ◽  
Asset Return ◽  
Confidence Levels ◽  
Return Distributions

The tail value at risk at level p, with p ∈ ( 0 , 1 ) , is a risk measure that captures the tail risk of losses and asset return distributions beyond the p quantile. Given two distributions, it can be used to decide which is riskier. When the tail values at risk of both distributions agree, whenever the probability level p ∈ ( 0 , 1 ) , about which of them is riskier, then the distributions are ordered in terms of the increasing convex order. The price to pay for such a unanimous agreement is that it is possible that two distributions cannot be compared despite our intuition that one is less risky than the other. In this paper, we introduce a family of stochastic orders, indexed by confidence levels p 0 ∈ ( 0 , 1 ) , that require agreement of tail values at risk only for levels p > p 0 . We study its main properties and compare it with other families of stochastic orders that have been proposed in the literature to compare tail risks. We illustrate the results with a real data example.

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