scholarly journals ESTIMATING THE HEDGE RATIOS

2016 ◽  
Vol 4 ◽  
pp. 229-234
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
Quantile Regression ◽  
Likelihood Estimation ◽  
Equal Weight ◽  
Minimal Risk ◽  
Hedge Ratio ◽  
Portfolio Returns ◽  
Hedge Ratios ◽  
Hedging Portfolio ◽  
T Copula ◽  
T Distribution

This paper examines the problem of hedging portfolio returns. Many practitioners and academicians endeavor to solve the problem of how to calculate the optimal hedge ratio accurately. In this paper we compare estimates of the hedge ratio from a classical approach of a linear quantile regression, based on selected quantiles as medians, with that of a non-linear quantile regression. To estimate the hedge ratios, we have used a calibrated Student t distribution for the marginal densities and a Student t copula of the portfolio returns using a maximum likelihood estimation. We created two portfolios of the assets, one for equal weight and another for optimal weight in respect of minimal risk. Our findings show that an assumption of Student t marginal leads to a better estimation of the hedge ratio.

scholarly journals Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

2019 ◽  
Vol 8 (3) ◽  
pp. 485-493
Keyword(s):  
Performance Measures ◽  
Minimum Mean Square Error ◽  
Multivariate Garch ◽  
Sample Analysis ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
T Distribution

Forecasting plays a crucial role in determining the direction of future trends and in making necessary investment decisions. This research presents the forecasting performance of three multivariate GARCH models: SGARCH, EGARCH, and GJR-GARCH based on Gaussian and Student’s t-distribution. The forecasting ability of the models is evaluated on the basis of forecasting performance measures: MAE, SSE, MSE, and RMSE. This is done by examining the hedged portfolios of three indices of NSE: NIFTY50, BANKNIFTY, and NIFTYIT. Daily data from Jan 2006 to Dec 2017 is taken and forecasts are conducted using out of sample data from Jan 2016-Dec 2017. Minimum mean square error (MMSE) forecasting method is used to generate conditional variance and covariance forecasts which in turn generate hedge ratios and corresponding hedged portfolio. Minimum variance hedge ratio framework of Ederington (1979) is used for hedging. The in-sample analysis shows that SGARCH with both the distribution performed better than the other models while out-of-sample analysis provides mixed results. EGARCH model assigns the lowest hedge ratio to NIFTY50 and BANKNIFTY while SGARCH model assigns the lowest hedge ratio to NIFTYIT. Forecasting performance measures show the least value for SGARCH and EGARCH model. In future these models are able to reduce maximum risk from the spot market. The results of this research has important implications for financial decision and policy makers.

Examining the Hedging Effectiveness of Futures Contracts over Pre and Post Financial Crisis Period: Evidence from National Stock Exchange of India

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Kapil Gupta ◽  
Mandeep Kaur
Keyword(s):  
Financial Crisis ◽  
Stock Exchange ◽  
Futures Contracts ◽  
Price Risk ◽  
Hedge Ratio ◽  
Crisis Period ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Time Invariant

Present study examines the efficiency of futures contracts in hedging unwanted price risk over highly volatile period i.e. June 2000 - December 2007 and January 2008 – June 2014, pre and post-financial crisis period, by using S&PC NXNIFTY, CNXIT and BANKNIFTY for near month futures contracts. The hedge ratios have been estimated by using five methods namely Ederingtons Model, ARMA-OLS, GARCH (p,q), EGARCH (p,q) and TGARCH (p,q). The study finds that hedging effectiveness increased during post crisis period for S&PC NXNIFTY and BANKNIFTY. However, for CNXIT hedging effectiveness was better during pre-crisis period than post crisis. The study also finds that time-invariant hedge ratio is more efficient than time-variant hedge ratio.

scholarly journals Kelly's Strategy Analysis in Optimizing Investment Portfolios in Foreign Exchange

2019 ◽  
Vol 8 (2S7) ◽  
pp. 245-250
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Optimal Portfolio ◽  
Minimal Risk ◽  
Investment Portfolio ◽  
Strategy Analysis ◽  
Average Value ◽  
Portfolio Returns ◽  
Strategy Model ◽  
Investment Portfolios

Investments in financial markets not only pay attention to promising profits, but also need to consider the risks that follow. Risks can be minimized by establishing an investment portfolio. This research was conducted with the aim of analyzing optimal portfolios on foreign exchange investments, so that investments made provide maximum returns at certain risks, or minimal risk on certain returns. The data analyzed in this study are foreign exchange traded at Bank Indonesia. Data analysis is carried out quantitatively using the Kelly Strategy model. The steps: (i) Calculation of individual foreign exchange returns, (ii) Determine the average value of individual foreign exchange returns, (iii) Determine the optimal portfolio using the Kelly strategy approach, and (iv) Determine portfolio returns and risks. Based on the results of the analysis obtained the allocation of weights that provide returns and risks to the optimal portfolio. A 95% USD currency is an optimal portfolio of the five currencies used. So that it can be used as a consideration for investors, in making investment decisions in the foreign exchange being analyzed.

scholarly journals Application of Iterative Maximum Weighted Likelihood Estimation in 3-D Target Localization

2019 ◽  
Vol 9 (22) ◽  
pp. 4921 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Liu ◽  
Wang
Keyword(s):  
Measurement Errors ◽  
Likelihood Estimation ◽  
Target Localization ◽  
Point Estimate ◽  
Equal Weight ◽  
Target Area ◽  
Water Tank ◽  
Weighted Likelihood ◽  
Weighted Likelihood Estimation ◽  
Weight Factors

Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method.

scholarly journals Dynamic portfolio rebalancing through reinforcement learning

2021 ◽  
Author(s):  
Qing Yang Eddy Lim ◽  
Qi Cao ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Prediction Model ◽  
Global Market ◽  
Alternative View ◽  
Minimal Risk ◽  
Market Conditions ◽  
Portfolio Rebalancing ◽  
Risk Levels ◽  
Portfolio Returns ◽  
Dynamic Portfolio

AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.

scholarly journals A New Generalized t Distribution Based on a Distribution Construction Method

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2413
Author(s):  
Ruijie Guan ◽  
Xu Zhao ◽  
Weihu Cheng ◽  
Yaohua Rong
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Iterative Algorithm ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Modified Method ◽  
The Em Algorithm ◽  
Likelihood Approach ◽  
T Distribution ◽  
Better Than

In this paper, a new generalized t (new Gt) distribution based on a distribution construction approach is proposed and proved to be suitable for fitting both the data with high kurtosis and heavy tail. The main innovation of this article consists of four parts. First of all, the main characteristics and properties of this new distribution are outined. Secondly, we derive the explicit expression for the moments of order statistics as well as its corresponding variance–covariance matrix. Thirdly, we focus on the parameter estimation of this new Gt distribution and introduce several estimation methods, such as a modified method of moments (MMOM), a maximum likelihood estimation (MLE) using the EM algorithm, a novel iterative algorithm to acquire MLE, and improved probability weighted moments (IPWM). Through simulation studies, it can be concluded that the IPWM estimation performs better than the MLE using the EM algorithm and the MMOM in general. The newly-proposed iterative algorithm has better performance than the EM algorithm when the sample kurtosis is greater than 2.7. For four parameters of the new Gt distribution, a profile maximum likelihood approach using the EM algorithm is developed to deal with the estimation problem and obtain acceptable.

scholarly journals Multivariate Skew t-Distribution: Asymptotics for Parameter Estimators and Extension to Skew t-Copula

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1059
Author(s):  
Tõnu Kollo ◽  
Meelis Käärik ◽  
Anne Selart
Keyword(s):  
Degrees Of Freedom ◽  
Data Modeling ◽  
Location Parameter ◽  
Asymptotic Distributions ◽  
Parameter Estimates ◽  
Parameter Distribution ◽  
Elliptical Distributions ◽  
T Copula ◽  
T Distribution ◽  
Parameter Estimators

Symmetric elliptical distributions have been intensively used in data modeling and robustness studies. The area of applications was considerably widened after transforming elliptical distributions into the skew elliptical ones that preserve several good properties of the corresponding symmetric distributions and increase possibilities of data modeling. We consider three-parameter p-variate skew t-distribution where p-vector μ is the location parameter, Σ:p×p is the positive definite scale parameter, p-vector α is the skewness or shape parameter, and the number of degrees of freedom ν is fixed. Special attention is paid to the two-parameter distribution when μ=0 that is useful for construction of the skew t-copula. Expressions of the parameters are presented through the moments and parameter estimates are found by the method of moments. Asymptotic normality is established for the estimators of Σ and α. Convergence to the asymptotic distributions is examined in simulation experiments.

scholarly journals Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

2022 ◽  
Vol 15 (1) ◽  
pp. 12
Author(s):  
Dean Leistikow ◽  
Yi Tang ◽  
Wei Zhang
Keyword(s):  
The Other ◽  
Conditional Correlation ◽  
Hedge Ratio ◽  
Cost Rate ◽  
Dynamic Conditional Correlation ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Rolling Window ◽  
Futures Price ◽  
Asset Classes

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

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