TARCH model-based dynamic hedging strategy of ADR portfolios

2022 ◽  
Vol 11 (2) ◽  
pp. 1
Author(s):  
Zdenek Zmeskal ◽  
Haochen Guo
Keyword(s):  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Model Based

Using VIX to Dynamically Hedge Portfolio Risk

GIS Business ◽  
1970 ◽  
Vol 13 (3) ◽  
pp. 15-22
Author(s):  
Richard Cloutier
Keyword(s):  
Asset Allocation ◽  
Market Timing ◽  
Investment Strategy ◽  
Market Risk ◽  
Portfolio Risk ◽  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Tactical Asset Allocation ◽  
Asset Class ◽  
Timing Strategies

Many investors accept buy and hold as their long-term investment strategy. However, during periods of heightened risk, staying disciplined can be problematic. Alternatively, market timing appeals to our emotions but is very difficult to employ successfully. Between these two extremes lies tactical asset allocation, where limited variances are allowed to take advantage of market conditions. Dynamic hedging is a form of tactical asset allocation. Instead of relying on future predictions of asset class returns, dynamic hedging strives to reduce portfolio risk when market risk is elevated. This paper presents a dynamic hedging strategy developed to accomplish this goal. It uses VIXs normal trading range to assess market risk. When VIX trades above its normal trading range and the upper Bollinger band, the dynamic hedging strategy is applied. The result is that portfolio risk is lowered when market risk is extreme. The application of this strategy provides better returns, lower volatility, and better downside protection than a strategic buy and hold allocation. It also avoids the deployment problems associated with market timing strategies.

Price risk management by using dynamic hedging based on advanced Black–Scholes model

2020 ◽  
Vol 07 (01) ◽  
pp. 2050011
Author(s):  
Peili Lu ◽  
Jiaqi Shen ◽  
Liheng Zhao ◽  
Haoyang Qin ◽  
Xunzhi Liu ◽  
...  
Keyword(s):  
Risk Management ◽  
Evaluation Method ◽  
Commodity Price ◽  
Price Risk ◽  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Replication Method ◽  
Black Scholes Model ◽  
Black Scholes ◽  
Price Risk Management

Price Risk Management plays an important role in Commodity trading and corporate purchasing or Sales plan. Futures are used to hedge the price risk which is linear, while options are used for the nonlinear one. This paper proposes an evaluation method of dynamic hedging strategy for corporate hedging commodity price risk based on advanced Black–Scholes Model. By using the inverse replication method, we get the dynamic hedging strategy which uses futures to replicate options. Finally, we apply the dynamic hedging strategy for corporate purchases and sales to either lower purchase cost or maintain the sales price.

scholarly journals Pricing and Hedging American-Style Options with Deep Learning

2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Sebastian Becker ◽  
Patrick Cheridito ◽  
Arnulf Jentzen
Keyword(s):  
Deep Learning ◽  
Optimal Stopping ◽  
Point Estimate ◽  
Learning Method ◽  
Call Option ◽  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Replication Errors ◽  
Hedging Strategies ◽  
American Style

In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.

DYNAMIC HEDGING OF INFLATION RISK

2009 ◽  
Vol 05 (01) ◽  
pp. 0950003
Author(s):  
UDO BROLL ◽  
STEFAN SCHUBERT
Keyword(s):  
Futures Markets ◽  
Price Level ◽  
Asset Price ◽  
Risk Averse ◽  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Inflation Risk ◽  
Optimal Dynamic ◽  
Futures Price ◽  
International Investors

National and international investors are exposed to risk, stemming from volatile asset prices and inflation uncertainty. However investors can enter futures markets to hedge against these risks. The paper develops a dynamic hedging model, where the evolution of asset price, price level and futures price and hence real wealth is stochastic. For a risk averse investor, optimal dynamic consumption and hedging strategy are derived and discussed.

The optimal dynamic hedging strategy for Nikkei 225 index and futures

2005 ◽  
Vol 8 (3) ◽  
pp. 477-491
Author(s):  
Chun-Da Chen ◽  
Mingchih Lee ◽  
Jer-Shiou Chiou
Keyword(s):  
Dynamic Hedging ◽  
Hedging Strategy ◽  
Optimal Dynamic

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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