A Comparative Analysis of Hedging Effectiveness of Won/Dollar Futures and NDF Contracts

2004 ◽  
Vol 12 (2) ◽  
pp. 73-99
Author(s):  
Won Cheol Yun ◽  
Hyun Jin An
Keyword(s):  
Exchange Rates ◽  
Ordinary Least Squares ◽  
Correction Model ◽  
Futures Contract ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Sample Data ◽  
Out Of Sample ◽  
Spot Exchange Rate ◽  
Better Than

This study compares the hedging effectiveness of domestic won/dollar futures and foreign non-deferable forward (NDF) contracts. We use an ex ante analysis based on out-of-sample data. In addition, the analysis is based on the inventory hedging scenario, adopted in most of previous studies. We estimated hedge ratios by using the various method of 1 : 1 hedging, ordinary least squares (OLS), and error correction model (ECM). The hedging period is expanded to include one to twelve months‘ In every aspect, the hedging effectiveness of won/dollar futures contract turns out to be better than that of NDF contract. However, the differences are not statistically significant at 10% level. This res비t stems from the fact that there exists a high correlation between spot exchange rate and futures or forward exchange rates, implying an evidence of co-movement between them.

Risk Management with KOSDAQ50 Index Futures Markets

2003 ◽  
Vol 11 (2) ◽  
pp. 51-79
Author(s):  
Gyu Hyeon Mun ◽  
Jeong Hyo Hong
Keyword(s):  
Garch Model ◽  
Minimum Variance ◽  
Price Risk ◽  
Risk Minimization ◽  
Index Futures ◽  
Hedging Strategies ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Sample Data ◽  
Hedging Performance

This paper studies hedging strategies that use the KOSDAQ50 index futures to hedge the price risk of the KOSDAQ50 index spot portfolio. This study uses the minimum variance hedge model and bivariate ECT-GARCH (1,1) model as hedging models, and analyzes their hedging performances. The sample period covers from January 31, 2001 to December 31, 2002. The most important findings may be summarized as follows. First, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios of the risk-minimization tend to be higher than those of GARCH model. Second, for the in-sample data, hedging effectiveness of GARCH model is higher than that of the risk-minimization, while for the out-of-sample data, hedging effectiveness of the risk-minimization with constant hedge ratios is not far behind the GARCH model in its hedging performance. Third, the hedging performance of KOSDAQ50 index futures is lower than that of KOSPI200 index futures, but higher than that of KTB futures. In conclusion, in the KOSDAQ50 index market, investors are encouraged to use the simple risk-minimization model to hedge the price risk of KOSDAQ50 spot portfolios.

Out-of-sample earnings forecasting for OLS and Theil–Sen models relative to a na.ı.ve no-change model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rick Neil Francis
Keyword(s):  
Ordinary Least Squares ◽  
Complex Model ◽  
Earnings Forecast ◽  
Change Model ◽  
Elementary Model ◽  
Content Type ◽  
Out Of Sample ◽  
Simple Question ◽  
Earnings Forecasting ◽  
Better Than

PurposeThe purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.Design/methodology/approachArchival in nature using a combination of regression analysis and binomial tests.FindingsThe binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.Research limitations/implicationsThe results are generalizable to US firms and may not extend to non-US firms.Practical implicationsThe TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.Social implicationsAwareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.Originality/valueAlthough a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

scholarly journals Estimation and Comparative of Dynamic Optimal Hedge Ratios of China Gold Futures Based on ECM-GARCH

2016 ◽  
Vol 8 (3) ◽  
pp. 236 ◽  
Author(s):  
Pujiang Chen ◽  
Zirong Zhuo ◽  
Jixiang Liu
Keyword(s):  
Error Correction ◽  
Dynamic Models ◽  
Garch Model ◽  
Error Correction Model ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Font Size ◽  
Correction Model ◽  
Hedge Ratios ◽  
Optimal Hedge

<span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">In order to avoid the risk of fluctuations in prices, commodity production operators develop future hedge, in which the evaluation of optimal hedge ratios are the core question. On the other hand, since gold plays an increasingly important role in Chinese economic activities, gold hedge become a hot topic. We employ gold future prices and spot gold prices in China market and the time period covered was January, 2014 to June, 2015 and calculate the optimal hedge ratios using different static and dynamic models. The static hedge model mainly used Ordinary Least Squares Regression</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">（</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">OLS</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">）</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">, Error Correction Model</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">（</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">ECM</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">）</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">and Vector Error Correction Model</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">（</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">VECM</span><span style="font-size: 10pt; font-family: 宋体; mso-bidi-font-size: 11.0pt; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman';">）</span><span style="font-size: 10pt; font-family: 'Times New Roman',serif; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US"> model. In addition, the dynamic hedge model mainly use bivariate GARCH model (BGARCH model). The results show that the efficiency of hedge of ECM-GARCH model is the best over the sample period.</span>

scholarly journals The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India

2011 ◽  
Vol 6 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Kailash Pradhan
Keyword(s):  
Least Square ◽  
Stock Index ◽  
Stock Index Futures ◽  
Vector Error Correction ◽  
Index Futures ◽  
Portfolio Returns ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Out Of Sample ◽  
Optimal Hedge

The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.

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 Exchange Rates and Monetary Fundamentals: What Do We Learn from Linear and Nonlinear Regressions?

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Guangfeng Zhang
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Correction Model ◽  
Exchange Rate Dynamics ◽  
Long Run ◽  
Exchange Rate Movements ◽  
Short Run ◽  
Us Dollar ◽  
Linear And Nonlinear ◽  
Japanese Yen

This paper revisits the association between exchange rates and monetary fundamentals with the focus on both linear and nonlinear approaches. With the monthly data of Euro/US dollar and Japanese yen/US dollar, our linear analysis demonstrates the monetary model is a long-run description of exchange rate movements, and our nonlinear modelling suggests the error correction model describes the short-run adjustment of deviations of exchange rates, and monetary fundamentals are capable of explaining exchange rate dynamics under an unrestricted framework.

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