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.

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.

Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

2017 ◽  
Vol 6 (2) ◽  
pp. 157-177 ◽  
Author(s):  
Thushari N. Vidanage ◽  
Fabrizio Carmignani ◽  
Tarlok Singh
Keyword(s):  
Econometric Model ◽  
Investment Decision ◽  
Jel Classification ◽  
Policy Formation ◽  
Return Volatility ◽  
Investment Decision Making ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
Emerging Capital Markets

The importance of return volatility forecasts in policy formation and investment decision-making in emerging countries is growing considerably. However, from an operational perspective, there is no consensus in the literature on which econometric model has the best forecasting performance. To shed new light on this issue, this article compares forecasting models for a selected group of emerging Asian economies: India, Malaysia, Pakistan, Sri Lanka, Singapore and Thailand. Model’s performance is tested using both in-sample and out-of-sample forecasting methods. It is found that a relatively simple asymmetric EGARCH model clearly outperforms other models. JEL Classification: G12, G17

scholarly journals Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check

2005 ◽  
Vol 25 (1) ◽  
pp. 43
Author(s):  
Leonardo Souza ◽  
Alvaro Veiga ◽  
Marcelo C. Medeiros
Keyword(s):  
Performance Measures ◽  
Gaussian White Noise ◽  
Garch Models ◽  
Parametric Test ◽  
Back Testing ◽  
Reality Check ◽  
Volatility Models ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Better Than

The important issue of forecasting volatilities brings the difficult task of back-testing the forecasting performance. As volatility cannot be observed directly, one has to use an observable proxy for volatility or a utility function to assess the prediction quality. This kind of procedure can easily lead to poor assessment. The goal of this paper is to compare different volatility models and different performance measures using White’s Reality Check. The Reality Check consists of a non-parametric test that checks if any of a number of concurrent methods yields forecasts significantly better than a given benchmark method. For this purpose, a Monte Carlo simulation is carried out with four different processes, one of them a Gaussian white noise and the others following GARCH specifications. Two benchmark methods are used: the naive (predicting the out-of-sample volatility by in-sample variance) and the Riskmetrics method

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

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 The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245904
Author(s):  
Viviane Naimy ◽  
Omar Haddad ◽  
Gema Fernández-Avilés ◽  
Rim El Khoury
Keyword(s):  
Value At Risk ◽  
Risk Measure ◽  
Canadian Dollar ◽  
Swiss Franc ◽  
Garch Models ◽  
Predictive Capacity ◽  
Australian Dollar ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Japanese Yen

This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

scholarly journals An approach to predict Spanish mortgage market activity using Google data

2019 ◽  
Vol 8 (4) ◽  
pp. 209
Author(s):  
Marcos González-Fernández ◽  
Carmen González-Velasco
Keyword(s):  
Prediction Accuracy ◽  
Mortgage Market ◽  
Sample Analysis ◽  
Market Activity ◽  
Out Of Sample

The aim of this paper is to use Google data to predict Spanish mortgage market activity during the period from January 2004 to January 2019. Thus, we collect monthly Google data for the keyword hipoteca, the Spanish expression for mortgage, and then, we perform a regression and an out-of-sample analysis. We find evidence that the use of Google data significantly improves prediction accuracy.

scholarly journals Forecasting Wheat Production in Pakistan

2008 ◽  
Vol 13 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Falak Sher ◽  
Eatzaz Ahmad
Keyword(s):  
Production Function ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forecasting Model ◽  
Future Prospects ◽  
Wheat Production ◽  
Steady Growth ◽  
Forecasting Performance ◽  
The Future ◽  
Out Of Sample

This study analyzes the future prospects of wheat production in Pakistan. Parameters of the forecasting model are obtained by estimating a Cobb-Douglas production function for wheat, while future values of various inputs are obtained as dynamic forecasts on the basis of separate ARIMA estimates for each input and for each province. Input forecasts and parameters of the wheat production function are then used to generate wheat forecasts. The results of the study show that the most important variables for predicting wheat production per hectare (in order of importance) are: lagged output, labor force, use of tractors, and sum of the rainfall in the months of November to March. The null hypotheses of common coefficients across provinces for most of the variables cannot be rejected, implying that all variables play the same role in wheat production in all the four provinces. Forecasting performance of the model based on out-of-sample forecasts for the period 2005-06 is highly satisfactory with 1.81% mean absolute error. The future forecasts for the period of 2007-15 show steady growth of 1.6%, indicating that Pakistan will face a slight shortage of wheat output in the future.

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