Estimation Window Research Articles

Futures-Based Forecasts of Cotton Prices: Beyond Historical Averages

Abstract This study explores several alternative specifications of futures-based forecasting models to improve existing approaches constrained by restrictive assumptions and limited information sets. In lieu of historical averages, our approaches use rolling regressions and include current market information reflected in the deviation of the current basis from its historical average. To mitigate potential challenges arising from nonstationarity and structural changes in the relationship between farm and futures prices, we employ a 5-year rolling estimation window. We find that a rolling regression approach offers significant improvements (as evidenced by our Modified Diebold–Mariano test) in the accuracy and information content of forecasts of cotton season-average prices (SAPs) mostly at short forecast horizons.

Impact of Stock Split on Stock Return in Companies Listed on the India Stock Exchange

The aim of this research is to evaluate the impact of a company's announcement, which in this case is a stock split, on the return of a company's stock listed on the Indian stock market. basing on this, the extent and significance of the effect is measured and examined through parametric and non-parametric tests. due to the aim of the paper is to test if the stock split will have a positive influence on the stock price around the implementation of the policy, we use the event study method and set regression model. In order to test the effect of the strategy, we picked estimation period and event period from each stock, and conduct regression analysis respectively. As suggested by the cumulative average AR value, the precision weighted CAAR and the ABHAR, the results imply that during the short window event of pre- and post-10 days of stock splits, the selected company have witnessed significant ARs so we reject H0 and accept H1, which suggests the presence of ARs within the event window. Similarly, other tests such as Patellz, Cross-sectional Test-t, Generalized Signz, Std. Cross-sectional Testz and Adjusted Patellz also suggest to reject H0 stating that there are ARs during the study window of the stocks splits. However, these findings are based on the 10-days event and 90-days estimation windows used for analysis and impact of the stocks splits on the stock returns of the selected company.

The Impact of Asymmetric Price Limits on Stock Portfolio Returns: Evidence from Indonesian Markets

This study examines the effect of asymmetric price limits policy on stock portfolio returns represented by equity mutual funds. An event study methodology was used to calculate abnormal returns (ARs) across four estimation models: constant mean, market-adjusted return, market model, and capital asset pricing model. The sample consists of 237 equity funds in Indonesia, with 7,968 observations in the estimation window and 4,977 in the event window. Statistically significant Average Abnormal Returns (AARs) and Cumulative Average Abnormal Returns (CAARs) were found through parametric and non-parametric tests. The findings are consistent with panel data analysis results. An increase in average returns and a decrease in standard deviation were found for sampled equity funds following the policy implementation. These findings highlight the importance of policy interventions in stabilizing financial markets during crises and offer valuable insights into stock portfolio performance before and after the policy.

The Impact of Asymmetric Price Limits on Stock Portfolio Returns: Evidence from Indonesian Markets

This study examines the effect of asymmetric price limits policy on stock portfolio returns represented by equity mutual funds. An event study methodology was used to calculate abnormal returns (ARs) across four estimation models: constant mean, market-adjusted return, market model, and capital asset pricing model. The sample consists of 237 equity funds in Indonesia, with 7,968 observations in the estimation window and 4,977 in the event window. Statistically significant Average Abnormal Returns (AARs) and Cumulative Average Abnormal Returns (CAARs) were found through parametric and non-parametric tests. The findings are consistent with panel data analysis results. An increase in average returns and a decrease in standard deviation were found for sampled equity funds following the policy implementation. These findings highlight the importance of policy interventions in stabilizing financial markets during crises and offer valuable insights into stock portfolio performance before and after the policy.

An Event Study on Impact of Russia-Ukraine War on Indian Banking Sector Stock

Aim: To study the impact of the Russia-Ukraine event on the performance of banking sector stocks. Study Designs: The descriptive statistical analysis has been done for six selected banks and is based on the Russia-Ukraine war. Study employs event analysis method for analysis. Place and Duration of Study: The secondary data has been downloaded from the NSE site, and the study duration is pre- and post-90 days from the event day and 120 days for the event window and 210 days for the estimation window has been considered. Methodology: The event analysis method has been used in which a paired t-test has been employed. We have checked significant at a p-value of 0.10 (10% level). Significant at p-value of 0.05 (5% level) Significant at p-value of 0.01 (1% level). Results: By examining a wide range of degrees of relationship between the two variables, we have tried to analyze how financial markets respond to changes in the warlike situation and how conditions impact asset prices. The results show the impact of the event before and after the event date. This analysis fills the gap in understanding event-specific impacts on financial institutions. Conclusion: The study provides insights into whether market behavior remained stable (null hypothesis accepted) or displayed volatility (alternate hypothesis accepted) across 90 days for global crises like the Russia-Ukraine war. The findings of the research have implications for the banking sector of India, policymakers, investors, and researchers. The research provides numerical and non-numerical information that other researchers can employ for cross-study analysis or for verifying theories. Lingering questions from the investors, including those in search of portfolio diversification strategies and the right time to invest. The discovery empowers the firms to have better policies inside and better relations outside, with customers and other stakeholders, such as during the change in monetary policy or in external shocks.

Optimal Joint Scheduling and Congestion Window Estimation for Congestion Avoidance in Mobile Opportunistic Networks

Optimal Joint Scheduling and Congestion Window Estimation for Congestion Avoidance in Mobile Opportunistic Networks

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

Integrating clustering and sequential analysis for improving the spectral density estimation and dependency structure of time series

Time series models (TSMs) are used to forecast events based on verified historical data that are widely applied to describe natural and social phenomena. The spectral density function (SDF) of the TSM has the benefit to determine trends, periodic patterns and behavior of observable events and is used in degree of memory (DOM) which is used to identify independent and dependent structures. Therefore, paying attention to the estimator of SDF (SDE) is the cornerstone and desired goal in the statistical properties of TSMs. This paper gives a novel perspective for improving the SDE then dependency structure of TSMs by integrating clustering and sequential analysis. The accuracy of the lag window estimator (LWE) and periodogram estimator (PE), which are commonly used as SDEs in practice, is examined both with and without integrating the new proposed technique in order to figure out the efficacy of the new technique. Several circumstances, including independent and dependent data structures, TSMs with long and short DOMs, different numbers of clusters, and different dataset sizes, are used to carry out the investigation. The main findings indicate that: (i) The SDEs (PE and LWE) perform better when combined with the new proposed technique than when used alone. (ii) Using the new technique in conjunction with the SDEs results in absolutely perfect estimators for independent processes. (iii) High efficiency estimators for short memory TSMs arise when the new proposed technique is combined with the SDEs in the dependent processes; however, high efficiency in long memory TSMs (LMTSMs) is not guaranteed. (iv) Using the new technique, the correlation estimator behavior between SDEs and frequencies improves significantly with increasing DOM values of LMTSMs. (v) By applying the new technique for LMTSMs, the DOM estimator provides results indicating significant dependence. This study demonstrates that there are other efficient ways to improve the estimator than estimate techniques.

Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques.

US Funds’ returns-based ESG extraction and implementation: a multifaceted quantile regression approach

ABSTRACT This study introduces a novel ESG intrinsic-based return factor and its application in asset pricing. This factor is extracted using a parallelized rolling window estimation and extreme value-weighted quantile portfolios. It carries a positive risk premium, indicating that investors are willing to assess its risk exposure. We further show that higher returns can be obtained in the top 30% quantiles using a long-only trading strategy. We apply a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to explain US fund returns and address the needs of investors seeking to optimize their investment strategies. This model surpasses traditional benchmark models by performing deep quantile estimation and considering the nonlinear relationships between fund returns and six firm-based characteristics. This approach empowers investors by explaining the core principles of impact investing and highlighting how our constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed. Highlights A sustainable smart beta factor construction is ensured ESG Long-only trading strategy grants higher returns in the top 30% quantiles Using ML, we further assessed the need to implement ESG factors into asset pricing Positive ESG risk premium reveals the investors’ interest in impact investing US market remains promising for positive ESG investment signals

Multivariate Regression Analysis of the Impact of Open Market Share Repurchase Activity on the Malaysian Market

This research thesis revolves around the need to understand the relationship between share repurchases and the share price behavior of companies listed on Bursa Malaysia on a more recent basis given the economic stability in Malaysia and throughout the world from 2017 to 2021. It started in the West and reached maturity in the 1980s in the United States, but it only began in Malaysia in the 1990s. The studies of share repurchase are still considered an infant in Malaysia as in other countries throughout the world, companies pursue share repurchases for several reasons mainly to signal undervaluation, reward shareholders, defend against the takeover, utilize financial resources efficiently, and many more motives that are interesting for researchers to look at. Unlike other studies that look at announcement dates, since an announcement does not mean the companies will implement the transactions, this research is important since it looks at and analyzes the actual effective repurchase day done by established firms which is are most crucial aspect in analyzing the consequences and the results are that it shows positive abnormal mean returns for most of the companies. Event study methodology was used since this is the best empirical research method and as empirical evidence to analyze abnormal returns from all the other studies. The days observed are 120 days before and after the announcement. Returns for the estimation window, event window (repurchase day), and post-event window were analyzed to see the effects of price movement on repurchase activities at Bursa Malaysia (BSKL).

Interconnectedness between real estate returns and sustainable investments: A cross-quantilogram and quantile coherency approach

The real estate sector is a substantial energy consumer, contributing to a large share of global greenhouse gas emissions. Despite the known links between the real estate sector and sustainable assets, studies exploring co-movements or dependence between them are still scarce. The present paper contributes to this challenging problem by capturing and analyzing the non-linear dependence over time and frequency between real estate returns and sustainable assets - green bonds, clean energy and environmental, social, and governance (ESG) stocks. We adopt a modeling approach that utilizes both the cross-quantilogram and the quantile coherency method. Our empirical results indicate unidirectional dependence from the real estate sector to most sustainable assets in both tails and under normal market conditions, although bidirectional dependence in the lower quantiles. Moreover, based on recursive window estimations, when the Covid-19 period is included, green bond and real estate returns are tail-dependent both during boom and bust periods. However, in non-pandemic periods, higher dependency is found during booms. Furthermore, the returns of the green bonds are less dependent on real estate returns compared to other sustainable assets. Our findings are potentially relevant for investment portfolio and public policy decision-making.

Dynamics of persistence in Brazilian economic uncertainty, expectation, and confidence indexes

This study examines the persistence dynamics in Brazilian economic uncertainty, confidence, and expectation indexes, in both business and consumer sides of the economy. Using rolling window estimations of the fractional integration parameter, we assess how economic turbulence influences the persistence of these indexes. We find that economic uncertainty in Brazil is non-stationary but mean-reverting, with its persistence only transitorily affected by economic and political crises. Interestingly, confidence and expectation indexes displayed hysteretic behavior, with multiple persistence regimes linked to crises. These results may be informative for policymakers about the perceptions of the public and businessmen to economic shocks and crises.

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimization-adaptive square root cubature Kalman filter (PSO-ASRCKF) is established to improve the SOC estimation ability and accuracy of LIBs. The filtering algorithm parameters are optimized to achieve high-precision SOC estimation. An improved ASRCKF with the PSO algorithm to optimize the moving estimation window is constructed to obtain the best adaptive window value. Different temperatures and initial SOC values are used to verify the proposed method under dynamic stress test (DST) and other conditions. The results show that the relative error is mainly distributed within 0.5 % when the SOC is stable. In addition, robustness and adaptability are verified with the root mean square error (RMSE) and the mean absolute error (MAE) values of 0.0019 and 0.0017, respectively, under the DST working condition. The experimental results show that the proposed method can achieve accurate SOC estimation under different temperatures, operating conditions, and initial SOC values.

EFFICIENT MARKET HYPOTHESIS DURING COVID-19 PANDEMIC: BRICS-T COUNTRIES

The impact of COVID-19 on financial markets has become a significant topic of research in the literature. Particularly, the declaration of the outbreak as a global pandemic by the World Health Organization on March 11, 2020, altered investors' risk perceptions and led to major fluctuations in stock markets. In this study, the response of stock indices in BRICS-T countries (Brazil, Russia, India, China, South Africa, and Türkiye) to the pandemic declaration was analyzed using the Event Study method. The event window was set as -5 to +5 days, and the estimation window was selected as the 90 days preceding the event. As a result of the analysis based on the comparison of normal and abnormal returns, it was found that all BRICS-T stock markets, except China, were not semi-strong form efficient. In addition, the results of the paired sample t-test applied to all stock markets demonstrated that the difference between abnormal returns calculated before and after COVID-19 was statistically significant. In this context, it was concluded that, unlike previous outbreaks, the COVID-19 pandemic led to significant fluctuations in financial markets. This study provides an important contribution to understanding the effects of the pandemic on emerging markets and examining investor behavior.

Realized Stock Market Volatility of the United States: The Role of Employee Sentiment

Using data for the United States (US) stock market covering the sample period from 2008:06 to 2020:12, we study the incremental predictive value of employee sentiment for the realized volatility of stock returns. In doing so, we control four different measures of investor sentiment and various macroeconomic and financial factors and uncertainties. We report results for several combinations of forecast horizons and estimation windows and find that employee sentiment contributes to forecast accuracy for several combinations.

Optimal Versus Naive Diversification in Commodity Futures Markets

ABSTRACTMotivated by the ongoing debate on whether optimal or naive diversification should be preferred when distributing wealth across investment alternatives, this article investigates how the choice of covariance estimator affects mean‐variance portfolio selection. In an environment tailored to ideal tradability, we construct optimal commodity futures portfolios based on 12 promising covariance matrix estimators and compare their out‐of‐sample investment performance to a simple, equally weighted investment strategy by means of bootstrap testing. We find that neither the naive allocation approach nor the advanced covariance estimators can outperform the traditional sample covariance matrix. Because this empirical result is robust to modifications of the research design (including alternative investigation periods, data frequencies, estimation window sizes, holding period lengths, weight constraint specifications, and transaction cost levels), it opposes the recurrent suggestion of the equity‐oriented literature that the sample covariance matrix should not be used for the purpose of portfolio optimization.

Abstract P345: Long-Term Blood Pressure Variability and Physical Performance Measures in Community-Dwelling Older Adults

High degrees of long-term, visit-to-visit blood pressure variability (BPV) independently predict increased risks of cardiovascular disease, cognitive decline, and frailty in older adults. Measures of physical performance such as gait speed and grip strength can predict loss of independence and mortality in older adults, but are not routinely measured in practice. Shared pathways of underlying physiologic deterioration may exist between high BPV and progressive loss of gait speed and grip strength. We examined the association between long term, visit-to-visit BPV and change in gait speed and grip strength in participants of the ASPirin in Reducing Events in the Elderly (ASPREE) study, a randomized primary prevention trial of low-dose aspirin conducted in 19,114 community-dwelling adults in Australia and the U.S. who were without independence-limiting physical disability at enrollment, and its extended long-term observational follow-up (ASPREE-XT). Long-term BPV was estimated in each participant using the standard deviation (SD) of the means of three within-visit systolic BPs obtained using a validated automated oscillometric BP monitor from the baseline and first two annual ASPREE visits. During ASPREE and continued into ASPREE-XT, participants underwent annual/biennial measures of grip strength using a Jamar hand grip dynamometer, and completed a timed 3 meter walk at normal pace. Linear mixed models were used to examine changes in gait speed and grip strength trajectories over a median of 5.2 years of follow-up after the second annual ASPREE visit (end of BPV estimation window). After covariate adjustment, the highest SD tertile of BPV, compared to the lowest SD tertile, was associated with greater declines in gait speed over time, and reduced grip strength cross-sectionally (see accompanying Table). Our findings suggest that long-term BPV is an important predictor of progressive loss of physical performance, particularly gait speed, in older adults. Since BP is routinely measured in practice, characterization of the visit-to-visit BPV may help identify older adults at risk of future declines in physical function and loss of independence.

Effects of Israel-Iran conflict: insights on global stock indices and currencies

PurposeThis study examines the immediate impact of the Israel-Iran conflict on global stock markets and currency pairs, focusing on how these effects vary by market maturity and geographic region.Design/methodology/approachThis study uses the event study method to examine the immediate effect of the Israel-Iran conflict. It uses the market model across a 252-day estimation window through −257, −6 trading days and an 11-day event window through −5, +5 trading days. The primary sample includes 73 stock market indices, 7 EURO currency pairs, 14 USD currency pairs, 6 GBP currency pairs, and 7 JPY currency pairs.FindingsThe findings suggest that (1) the global stock markets are adversely affected by the Israel-Iran conflict, (2) the JPY, GBP, and EURO currency pairs are least affected, (3) the USD currency pairs exhibit positive abnormal returns suggesting flight to safety, (4) the frontier and standalone markets experience most adverse effects, followed by developed and emerging markets, (5) the pan-American stock markets experience more pronounced effects of the conflict, followed by the Europe, Middle East, and African stock markets and the Asia Pacific stock markets.Research limitations/implicationsThe findings advise investors to manage risk during geopolitical uncertainty through diversification and hedging. Policymakers should monitor developments and enact responsive measures. Market participants can capitalize on insights for strategic investment.Originality/valueThe study contributes to the extant war literature by exploring the impact of the Israel-Iran conflict on global stock markets and currency pairs. This study serves as the first to examine the effects of the escalating conflict due to Iran’s attack on Israel.

The role of gold in terrorism: Risk aversion or financing source?

Based on the perspective of terrorism, this study uses a bootstrap full-sample causality test and sub-sample rolling window estimations to explore the risk aversion capability and terrorist financing capability of gold's time-varying dimension. The results suggest that terrorist incidents (defined by the number of terrorist attacks) and terrorist brutality (measured by the number of fatalities produced by terrorist attacks) are important factors in explaining changes in gold prices, but their effects differ. With extensive media coverage, the cause of investors' panic has changed from terrorist incidents to terrorist brutality. The results from some sub-sample periods support the capital asset pricing model by showing that terrorist attacks push up gold prices through a “terror premium.” Considering reverse causality, gold price can be used as an early warning of terrorist attacks, but its terrorist financing function is not yet proven. In the context of increasingly serious terrorism, studying the interaction mechanism between terrorist attacks and gold prices can help investors reasonably plan investments and provide insights for the government on combatting terrorism and maintaining world peace.