Negative Price Research Articles

Connectedness of Carbon Price and Energy Price under Shocks: A Study Based on Positive and Negative Price Volatility

We calculate both positive and negative price volatilities based on Realized Semi-variance (RS) in major economies’ carbon and energy markets with daily data from 1 July 2013 to 31 May 2023. Subsequently, we construct a network using the Elastic-Net-VAR model to analyze the contagion of price volatilities and examine how shocks affect the connectedness between these markets’ price volatilities using Local Projection. The following findings are presented: (1) There exists a robust correlation between carbon price volatilities and energy price volatilities, with time-varying overall network connectedness ranging from 21.54% to 83.34%. (2) Carbon markets primarily act as recipients of price volatilities, while energy markets serve as initiators. (3) The spillover effects and inflow of negative price volatilities are more pronounced compared to those of positive price volatilities. This is attributed to the fact that declining prices often indicate a market downturn, leading to the easy dissemination of adverse news across interconnected markets. Concurrently, increasing fragility diminishes its resilience against risks. (4) Shocks have a significant influence on the connectedness between carbon prices and energy prices, with different mechanisms at play under different shocks. The COVID-19 pandemic has increased the connectedness between carbon markets and energy markets primarily through common exposure mechanisms. Conversely, geopolitical risks reduce network connectedness by decreasing price complementarity.

Implied volatility slopes and jumps in bitcoin options market

This paper derives a theoretical relation between the left and right slopes of the implied volatility curve with negative and positive price jumps. Empirical analysis using bitcoin options tick-by-tick data from Deribit exchange supported the theoretical findings that negative and positive jumps have reversal impacts on bitcoin options' implied volatility slopes even after the control of net-buying-pressure and realized positive and negative skewness measures.

Impact of Contracts for Differences for non-carbon electricity generation on efficiency of electricity market

In response to the recent energy crisis, the European Commission proposed two-way Contracts for Differences (CfDs) to secure renewable energy investments and protect consumers. The proposed CfDs, however, raised concerns regarding the potential impact on electricity prices. This study examines the effects of various CfD design elements on day-ahead electricity prices, welfare, and renewable energy production. Using a partial equilibrium model of an international electricity market including thirteen European countries, we find a trade-off between stimulating renewable electricity producers to respond to negative prices, and ensuring producer certainty. We also show potential market distortions for CfDs with longer-term reference prices, particularly when strike prices deviate from expectations. Additionally, our study underscores the interactions among various players in the electricity market, in particular storage operators and international traders, and how these interactions can mitigate potential distortions from CfDs. Therefore, policymakers should carefully consider the design elements of CfDs, and weigh the promotion of renewable energy production against the efficiency of markets. Moreover, a proper reference price should be selected, to avoid further increasing electricity prices in periods with high electricity prices.

Optimization of curtailment intervals of wind turbines through assessment of measured loads during start-up and shutdown events

There is an increasing amount of curtailment of wind parks due to grid overload or negative market prices. Operators need to also account for the effect of curtailment on component loads, wear, and lifetime. This paper investigates curtailment leading to turbine shutdown. A novel methodology to optimize the length of curtailment intervals based on load neutrality is introduced. Measurement data from the structural health monitoring system of an onshore and an offshore wind turbine are used to quantify effects of curtailment on loads and wear of tower, blade, and pitch system. Shutdown and start-up events increase loads on the tower and pitch system. Blades benefit from lower edgewise loads and reduced erosion. Results show that load neutrality for the tower is reached on average after 63 min of idling for the onshore and after 200 min for the offshore wind turbine evaluated (not generalizable). An optimal curtailment strategy shall use flexible time intervals but ensure load neutrality on yearly average. This can serve as a simple and valuable guideline for wind farm operators to evaluate the criticality of curtailment times in terms of asset fatigue life. Future research shall investigate curtailment strategies that also include deration of power output.

Exchange rates, uncertainty, and price-setting: Evidence from CPI microdata

We analyze the role of exchange rates and uncertainty for inflation and price-setting behavior. To this end, we exploit micro-level data underlying the Consumer Price Index of Chile for the period 2010–2018, and employ a standard dynamic distributed lags specification. We find that uncertainty and exchange rates are positively associated with product-level inflation. Then, we analyze the effect on different dimensions of price-setting. The relation with the frequency of positive (negative) price changes at the variety-establishment level is positive (negative). The results for frequencies are quantitatively important, highlighting this adjustment margin against movements in exchange rates and uncertainty. In contrast, we find little association with the magnitudes of price adjustments.

PUBLIC DISCLOSURE OF INFORMATION SECURITY BREACH INCIDENTS: SHORT-TERM STOCK MARKET REACTION ON INDIAN LISTED FIRMS

ABSTRACT Data breach incidents are growing by the day and firms struggle to detect, defend, and respond to such breaches. Nowadays, security breaches are considered one of the major concerns for corporate organizations around the world. Hence, it is essential to assess the impact of such breaches on organizations. This article reports stock price reaction due to public disclosure of information security breach (ISB) incidents on publicly traded firms of India. Using the event study methodology on a sample of 120 publicly announced ISB incidents between January 2004 and April 2019 pertaining to 69 publicly listed firms of India, we found that exposure to an ISB incident exacerbates negative stock price reactions based on both one-factor market model and Fama-French three-factor model. On average, breached firms lost 0.55% of their market value within two days post the announcement of ISB incidents. Further, we found some factors that significantly negatively impacted Cumulative Abnormal Return (CAR). Important emerging factors such as type of compromised data, sentiment, subjectivity, and remedial strategy significantly impact CAR. According to our study findings, we suggest that firms should mention remedial measures in terms of apology and/or compensation in the ISB disclosure. Furthermore, the result indicates that investors penalize listed firms for subsequent ISB incidents. Thus, our findings may guide Chief Information Officers (CIOs), information security managers, and IT managers of publicly traded firms to devise various strategies in terms of IT security measures and content of the ISB disclosure.

The Impact of CSI SEEE Carbon Neutral Index Launched on Order Aggressiveness

In the context of carbon peaking and carbon neutrality goals, in order to clarify the investment direction for investors, China Securities Index Co., Ltd. (CSI) has collaborated with the Shanghai Environmental Energy Exchange to develop the CSI SEEE Carbon Neutral Index (CSCNI), which has also played a leading role in the subsequent preparation of the Green Finance Index. The launch of this index has sparked research interest among scholars in stimulating investor order aggressiveness. This study employs event study methodology to examine the impact of the CSCNI launch on order aggressiveness. The sample companies are categorized into two groups: deep low-carbon and high-carbon reduction, with a focus on studying buy and sale order aggressiveness. The results indicate that the launch of CSCNI has mobilized order aggressiveness but has led to a negative stock price effect as investors anticipate an increase in environmental costs for the sample companies. Furthermore, we reveal that the long-term growth potential of the deep low-carbon field is more promising compared to the high-carbon reduction sector, making stocks in the deep low-carbon field more attractive. The launch of CSCNI has shown contrasting effects on the buy and sale order aggressiveness of investors, with the impact of the index announcement being more significant on the sample companies. This research provides valuable insights for evaluating the impact of green finance indices and contributes to the understanding of internal mechanisms. It provides an important reference for financial regulators to evaluate the development of the current green index. At the same time, it expands the domestic research on order aggressiveness, which studies the action mechanism of the stock price effect of the green stock index from the perspective of order aggressiveness.

Digital Technologies, Investor Sentiment, Limits to Arbitrage and Value of Firm Cash Holdings in China's A Stock Market is Good

Cash is a firm’s most liquid asset and this asset can add firm value by mitigating firm financial constraints, reducing debt default risk and opportunity costs. But the positive and negative asset pricing effect of cash holdings are related to digital technologies, sustainable performance and investors’ sentiment and limits to arbitrage. The paper finds the digital technologies and sustainable performance, both are negatively associated with the value of cash holding. The current study also scrutinized that firm’s cash holding value appears to be positive in China’s A stock market, and this asset pricing effect is more significant in periods when investor sentiment is high and in firms confronting high level of limits to arbitrage. This research provides evidences on the asset pricing effect of firm cash holdings and on how inefficient capital market affects this asset pricing effect. The findings of this research are useful for firms) improving cash management and have implications for supervising department to improve stock market efficiency.

Extreme co-movements between decomposed oil price shocks and sustainable investments

This paper investigates the extreme co-movements between three types of decomposed oil price shocks, e.g., supply shock, demand shock, and risk shock, and sustainable investments, using the generalized extreme value - autoregressive conditional Fréchet - tail quotient correlation coefficient (GEV-AcF-TQCC) analytical framework. The empirical results have identified that there are regular extreme co-movements between the three types of oil shocks and sustainable investments under extreme market conditions, especially, during the 2014–2016 oil crisis, the COVID-19 pandemic and the event of negative oil prices in 2020, according to the dynamic tail quotient correlation coefficient (TQCC) method. Specifically, the upside and downside trends of supply shocks, the downside trend of demand shocks, and the upside trend of risk shocks act as transmitters of the tail risks between oil prices and sustainable investments. The results also have demonstrated that the tail risk indexes for both the upside and downside trends of decomposed oil price shocks and sustainable investments have time-varying and asymmetric characteristics. Additionally, the static TQCC results have validated that among the three types of oil shocks, demand shocks exhibit the most pronounced tail risk spillover effects on sustainable investments, followed by supply and risk shocks, whose tail risk spillovers are close to each other. A series of alternative analyses ensure the robustness of the findings presented in this paper, which can offer valuable guidance and insights to policymakers and financial investors.

The shape of the Treasury yield curve and commodity prices

We decompose the U.S. yield curve into three latent factors – the level, slope and curvature – and explore the information content of the yield curve regarding the future evolution in oil, coal, copper, ethanol, gold, heating oil, natural gas, palladium, platinum, silver and zinc prices. Using data from January 1986 to November 2021, we find that the shape of the term structure is very informative about future innovations in these commodities. Results indicate that shocks to the level, which represents long-term expectations about inflation, predict positive (negative) price (volatility) changes. Shocks to the slope and curvature, which are interpreted as part of the business cycle conditions, predict negative price changes. These results are manifested mainly in the period after the financialization era in 2004. Our findings have far-reaching implications for investors, policymakers and firms involved in the mining industry.

Variance Decomposition and Cryptocurrency Return Prediction

Abstract This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

Risk‐Constrained Optimal Scheduling in Water Distribution Systems Toward Real‐Time Pricing Electricity Market

AbstractIn recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting.

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field.

Do share offerings increase payouts?

This paper investigates the effects of pure secondary share offerings, which decrease ownership concentration without raising funds, to highlight the corporate governance effect of equity offerings. We find that firms conducting secondary offerings significantly increase dividend payouts after the transaction. Although the announcement of secondary offerings receives negative stock price reactions in the short run, we witness a reversal and positive performance in the long term. While previous studies commonly suggest equity offerings cause negative stock returns, our results reveal that these transactions make managers care about minority shareholder wealth by unwinding ownership concentration.

Market-based oil spill(overs): Market reactions to the energy windfall tax announcements and disclosures in the United Kingdom

We investigate the market reaction to a windfall tax regulation announcement for oil and gas firms in the UK. We find that firms subject to the windfall tax experience a significant abnormal price effect of 6.0 to 7.3 %. The effect is concentrated among large and more profitable firms. The results also show that firms voluntarily disclose more precise information after the regulation to offset the negative abnormal price reaction at the windfall tax announcement date. Evidence suggests that these disclosures are largely effective at allaying the price effects associated with increased investor disagreement. Moreover, our analyses show that firms within the same industry experience a negative spillover effect and decreased investor demand associated with heightened policy uncertainty about the implementation of windfall tax regulation. This version: February 2024

Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets

Electricity market liberalization and the absence of cost-efficient energy storage technologies have led to the transformation of state-owned electricity companies into complex electricity market entities, each having a different time horizon. Deregulation has intensified competition, giving rise to increased uncertainty caused by a multitude of interrelated exogenous factors, resulting in unexpected fluctuations in electricity prices. As a consequence, market participants encounter elevated risks and seek effective mitigation strategies. In this paper, the challenges described in the literature are addressed by studying price distribution histograms in the German and Finnish electricity markets. The objective is to identify normal price intervals that can serve as a foundation for an integrated Day-Ahead price forecasting methodology. A novel approach utilizing the Extreme Learning Machine in combination with Bootstrap intervals is proposed and applied to both markets. The findings demonstrate that Bootstrap intervals effectively capture normal prices, whereas extremely high prices typically align with the upper limits of Bootstrap intervals. Conversely, negative prices tend to fall outside the lower boundaries of the intervals. In order to assess the performance of the proposed methodology, a comparative analysis of its forecasting accuracy against the well-established Generalized AutoRegressive Conditional Heteroskedasticity and AutoRegressive Fractionally Integrated Moving Average models is conducted. In addition, both the computational efficiency and forecasting accuracy of the Extreme Learning Machine in comparison to the Artificial Neural Network are assessed. The results reveal the superior efficiency of the Extreme Learning Machine. The developed forecasting model could potentially assist market participants in making well-informed decisions and executing optimal bidding strategies in response to various scenarios before the Day-Ahead market closes. Notably, the proposed methodology transcends the limitations of fixed price thresholds and effectively addresses market nuances, including the occurrence of negative prices, thus offering a more comprehensive approach for electricity price forecasting.

Bond Price Fragility and the Structure of the Mutual Fund Industry

Abstract We conjecture that mutual funds with large shares of outstanding bond issues are more inclined to internalize the negative price spillovers of fire sales and thus sell their holdings in those issues, to a lower extent, when they experience redemptions. We provide evidence consistent with this conjecture and further show that ownership concentration limits bonds’ exposures to flow-induced fire sales. We exploit variation in negative spillovers arising from the Fed’s SMCCF to confirm the economic mechanism and explore our findings’ implications for fund performance and fire-sale spillovers to other funds.

Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques

This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-one-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.

Expected idiosyncratic entropy

PurposeWe propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.Design/methodology/approachWe estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.FindingsWe find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.Originality/valueWe propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

Enduring relief or fleeting respite? Bitcoin as a hedge and safe haven for the US dollar

Can technology protect investors from extreme losses? This paper investigates the short- and long-run hedging and safe haven properties of Bitcoin for the US dollar over the period 2010–2023, incorporating the COVID-19-related market turmoil. Our findings reveal that (i) Bitcoin acts as a strong hedge for all US dollar currency pairs examined, (ii) Bitcoin functions as a weak safe haven for the US dollar at short investment horizons, as indicated by a limited relationship during acute negative price movements, (iii) Bitcoin, instead of acting as a safe haven may, instead, increase aggregate risk at long horizons during periods of extreme losses. The analysis, performed using a series of horizon-dependent econometric tests, provides evidence of some US dollar risk-reduction benefits from Bitcoin but limited potential for enduring relief from long-run extreme negative US dollar rate movements.