scholarly journals Dynamic Hedging Based on Fractional Order Stochastic Model with Memory Effect

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qing Li ◽  
Yanli Zhou ◽  
Xinquan Zhao ◽  
Xiangyu Ge
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Fractional Order ◽  
Minimum Variance ◽  
Hedge Ratio ◽  
Out Of Sample ◽  
Sample Test ◽  
Optimal Dynamic ◽  
Optimal Hedge ◽  
With Memory

Many researchers have established various hedge models to get the optimal hedge ratio. However, most of the hedge models only discuss the discrete-time processes. In this paper, we construct the minimum variance model for the estimation of the optimal hedge ratio based on the stochastic differential equation. At the same time, also by considering memory effects, we establish the continuous-time hedge model with memory based on the fractional order stochastic differential equation driven by a fractional Brownian motion to estimate the optimal dynamic hedge ratio. In addition, we carry on the empirical analysis to examine the effectiveness of our proposed hedge models from both in-sample test and out-of-sample test.

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

scholarly journals Fractional Order Stochastic Differential Equation with Application in European Option Pricing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qing Li ◽  
Yanli Zhou ◽  
Xinquan Zhao ◽  
Xiangyu Ge
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Option Pricing ◽  
Memory Effect ◽  
Fractional Order ◽  
Mean Value ◽  
European Option ◽  
European Option Pricing ◽  
Proposed Model ◽  
Pricing Formula

Memory effect is an important phenomenon in financial systems, and a number of research works have been carried out to study the long memory in the financial markets. In recent years, fractional order ordinary differential equation is used as an effective instrument for describing the memory effect in complex systems. In this paper, we establish a fractional order stochastic differential equation (FSDE) model to describe the effect of trend memory in financial pricing. We, then, derive a European option pricing formula based on the FSDE model and prove the existence of the trend memory (i.e., the mean value function) in the option pricing formula when the Hurst index is between 0.5 and 1. In addition, we make a comparison analysis between our proposed model, the classic Black-Scholes model, and the stochastic model with fractional Brownian motion. Numerical results suggest that our model leads to more accurate and lower standard deviation in the empirical study.

scholarly journals Strategic Asset Allocation Of Credit Guarantors

2015 ◽  
Vol 31 (5) ◽  
pp. 1823
Author(s):  
Dong-Woo Rhee ◽  
Hyoung-Goo Kang ◽  
Soo-Hyun Kim
Keyword(s):  
Public Policy ◽  
Asset Allocation ◽  
Minimum Variance ◽  
Risk Return ◽  
Out Of Sample ◽  
Sample Test ◽  
Prior Literature ◽  
The Mean ◽  
Risk Contribution ◽  
Mean Variance

<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

Hedging and Optimal Hedge Ratios for International Index Futures Markets

2009 ◽  
Vol 12 (04) ◽  
pp. 593-610 ◽  
Author(s):  
Cheng-Few Lee ◽  
Kehluh Wang ◽  
Yan Long Chen
Keyword(s):  
Futures Markets ◽  
Minimum Variance ◽  
Stock Index ◽  
Stock Index Futures ◽  
Hedge Ratio ◽  
Index Futures ◽  
Static Hedging ◽  
Hedge Ratios ◽  
Optimal Hedge ◽  
Mean Variance

This empirical study utilizes four static hedging models (OLS Minimum Variance Hedge Ratio, Mean-Variance Hedge Ratio, Sharpe Hedge Ratio, and MEG Hedge Ratio) and one dynamic hedging model (bivariate GARCH Minimum Variance Hedge Ratio) to find the optimal hedge ratios for Taiwan Stock Index Futures, S&P 500 Stock Index Futures, Nikkei 225 Stock Index Futures, Hang Seng Index Futures, Singapore Straits Times Index Futures, and Korean KOSPI 200 Index Futures. The effectiveness of these ratios is also evaluated. The results indicate that the methods of conducting optimal hedging in different markets are not identical. However, the empirical results confirm that stock index futures are effective direct hedging instruments, regardless of hedging schemes or hedging horizons.

Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures

2017 ◽  
Vol 6 (2) ◽  
pp. 108-131 ◽  
Author(s):  
Gurmeet Singh
Keyword(s):  
Stock Exchange ◽  
Futures Market ◽  
Minimum Variance ◽  
Econometric Models ◽  
Sample Period ◽  
Hedge Ratio ◽  
Index Futures ◽  
Hedging Effectiveness ◽  
Egarch Model ◽  
Optimal Hedge

This study attempts to study and suggest an optimal hedge ratio to Indian investors and traders by examining the three main indices of National Stock Exchange of India (NSE), namely, NIFTY, Bank NIFTY, and IT NIFTY, over the sample period from January 2011 to December 2015. The present study estimated the hedge ratio through six econometric models, namely, OLS, GARCH, EGARCH, TARCH, VAR, and VECM, in the minimum variance hedge ratio framework as suggested by Ederington (1979). The findings of the present study confirm the theoretical properties of Indian cash and futures market and suggest that the optimal hedge ratio estimated through EGARCH model was lowest for the NIFTY and Bank NIFTY, and that for IT NIFTY, the OLS model shows the lowest optimal hedge ratio as compared to that estimated through other models.

scholarly journals Energy Commodities: A Review of Optimal Hedging Strategies

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3979 ◽  
Author(s):  
Halkos ◽  
Tsirivis
Keyword(s):  
Expected Utility ◽  
Utility Maximization ◽  
Minimum Variance ◽  
Energy Market ◽  
Hedge Ratio ◽  
Hedging Strategies ◽  
Current Review ◽  
Economic Climate ◽  
Expected Utility Maximization ◽  
Optimal Hedge

Energy is considered as a commodity nowadays and continuous access along with price stability is of vital importance for every economic agent worldwide. The aim of the current review paper is to present in detail the two dominant hedging strategies relative to energy portfolios, the Minimum-Variance hedge ratio and the expected utility maximization methodology. The Minimum-Variance hedge ratio approach is by far the most popular in literature as it is less time consuming and computationally demanding; nevertheless by applying the appropriate multivariate model Garch family volatility model, it can provide a very reliable estimation of the optimal hedge ratio. However, this becomes possible at the cost of a rather restrictive assumption for infinite hedger’s risk aversion. Within an uncertain worldwide economic climate and a highly volatile energy market, energy producers, retailers and consumers had to become more adaptive and develop the necessary energy risk management and optimal hedging strategies. The estimation gap of an optimal hedge ratio that would be subject to the investor’s risk preferences through time is filled by the relatively more complex and sophisticated expected utility maximization methodology. Nevertheless, if hedgers share infinite risk aversion or if alternatively the expected futures price is approximately zero the two methodologies become equivalent. The current review shows that when evidence from the energy market during periods of extremely volatile economic climate is considered, both hypotheses can be violated, hence it becomes reasonable that especially for extended hedging horizons it would be wise for potential hedgers to take into consideration both methodologies in order to build a successful and profitable hedging strategy.

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