Results 11 to 20 of about 143,218 (278)

Using Neural Networks to Price and Hedge Variable Annuity Guarantees

open access: yesRisks, 2018
This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations ...
Daniel Doyle, Chris Groendyke
doaj   +1 more source

Hedging strategies and minimal variance portfolios for European and exotic options in a Levy market [PDF]

open access: yes, 2008
This paper presents hedging strategies for European and exotic options in a Levy market. By applying Taylor's Theorem, dynamic hedging portfolios are con- structed under different market assumptions, such as the existence of power jump assets or moment ...
Olhede, Sofia   +2 more
core   +6 more sources

Hedging Effectiveness under Conditions of Asymmetry [PDF]

open access: yes, 2011
We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using crude oil futures contracts.
Fishburn P., Jim Hanly, John Cotter
core   +3 more sources

Relaxing the Assumptions of Minimum-Variance Hedging

open access: yesJournal of Agricultural and Resource Economics, 1996
The most important minimum-variance hedging ration assumptions are (a) that production is deterministic and (b) that all of the agent's wealth is invested in the cash position. Stochastic production greatly reduces optimal hedge ratios.
Sergio H. Lence
doaj   +1 more source

On Arbitrage and Duality under Model Uncertainty and Portfolio Constraints [PDF]

open access: yes, 2015
We consider the fundamental theorem of asset pricing (FTAP) and hedging prices of options under non-dominated model uncertainty and portfolio constrains in discrete time.
Bayraktar, Erhan, Zhou, Zhou
core   +2 more sources

Hedging performance of multiscale hedge ratios [PDF]

open access: yesJournal of Futures Markets, 2019
AbstractIn this study, the wavelet multiscale model is applied to selected assets to hedge time‐dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in‐sample and out‐of‐sample portfolio variances, the wavelet‐based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest ...
Jahangir Sultan   +3 more
openaire   +1 more source

The influence of the spillover between futures and spot markets on hedging policy: evidence from Chinese stock markets

open access: yesFrontiers in Physics, 2023
This paper examines the impact of risk spillovers between Chinese stock and futures markets on stock hedging policies. This paper calculates the correlation between the overall risk spillover and the hedging ratio, effectiveness, and hedging cost based ...
Kai Shi, Junlian Gong
doaj   +1 more source

Effective Basemetal Hedging: The Optimal Hedge Ratio and Hedging Horizon [PDF]

open access: yesJournal of Risk and Financial Management, 2008
This study investigates optimal hedge ratios in all base metal markets. Using recent hedging computation techniques, we find that 1) the short-run optimal hedging ratio is increasing in hedging horizon, 2) that the long-term horizon limit to the optimal hedging ratio is not converging to one but is slightly higher for most of these markets, and 3) that
Dewally, Michael, Marriott, Luke
openaire   +2 more sources

A model for hedging load and price risk in the Texas electricity market [PDF]

open access: yes, 2012
Energy companies with commitments to meet customers’ daily electricity demands face the problem of hedging load and price risk. We propose a joint model for load and price dynamics, which is motivated by the goal of facilitating optimal hedging decisions,
Aïd   +26 more
core   +1 more source

Deep hedging

open access: yesQuantitative Finance, 2018
We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case
Buehler, H.   +3 more
openaire   +3 more sources

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