Optimal Out-of-Sample Forecast Evaluation under Stationarity

2021 ◽  
Author(s):  
Filip Staněk
Keyword(s):  
Forecast Evaluation ◽  
Out Of Sample

scholarly journals MODELLING THE CONDITIONAL CO-MOVEMENTS OF PAKISTAN AND INTERNATIONAL STOCK MARKETS

2021 ◽  
Vol 9 (3) ◽  
pp. 295-307
Author(s):  
Ali Akbar Pirzado ◽  
Naeem Ahmed Qureshi ◽  
Imran Khan Jaoti ◽  
Komal Arain ◽  
Riaz Ali Buriro
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Information Criterion ◽  
Forecast Evaluation ◽  
Conditional Correlation ◽  
International Stock Markets ◽  
Out Of Sample ◽  
The Government ◽  
New Policies

Purpose of the study: This study assesses and evaluates the conditional co-movements and dynamic conditional correlation of the Pakistan Stock Exchange (PSX) with other Stock Market. Methodology: DCC-GARCH model has been applied due to its feasibility to model the covariance as a function of correlation and variance together. Main findings: The findings of the research suggest that the Pakistani Stock Exchange (PSX) is highly volatile compared to two other selected stock markets. In-sample fitting, the study has selected the DCC-GARCH (1, 1) model based on information criterion, conversely, the criterion used for out-of-sample forecast evaluation such as MSFE, RMSFE, MAPE, selected the DCC (2,1)-GARCH (1,1). Application of the study: This study is very useful for the Pakistan stock market and other international selected stocks markets until and unless the government of Pakistan and other governments will devise new policies which may open new opportunities to investors. Novelty/ Originality of the study: This study has a great potential in the Pakistani stock market to offer investors to several foreign and domestic investors, allowing them to hold Pakistan as well as foreign and local stocks all major benefits.

scholarly journals “A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 171
Author(s):  
Nicolas Hardy
Keyword(s):  
Asymptotic Distribution ◽  
Null Model ◽  
Critical Values ◽  
Forecast Evaluation ◽  
Nuisance Parameters ◽  
Nested Model ◽  
Nominal Size ◽  
Out Of Sample ◽  
Size Distortions ◽  
Encompassing Test

Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested model may severely distort the size properties of traditional out-of-sample tests in economic forecasting. Not surprisingly, these size distortions depend on the magnitude of the bias and the persistency of the additional predictors. We consider two different cases: (i) There is both in-sample and out-of-sample bias in the nested model. (ii) The bias is present exclusively out-of-sample. To address the former case, we propose a modified encompassing test (MENC-NEW) robust to a bias in the null model. Akin to the ENC-NEW statistic, the asymptotic distribution of our test is a functional of stochastic integrals of quadratic Brownian motions. While this distribution is not pivotal, we can easily estimate the nuisance parameters. To address the second case, we derive the new asymptotic distribution of the ENC-NEW, showing that critical values may differ remarkably. Our Monte Carlo simulations reveal that the MENC-NEW (and the ENC-NEW with adjusted critical values) is reasonably well-sized even when the ENC-NEW (with standard critical values) exhibits rejections rates three times higher than the nominal size.

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

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