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The purpose of this paper is to investigate the possibility of monitoring stress on stock markets from the perspective of a central counterparty (CCP). Due to their balanced positions, CCPs are exposed to extreme price movements in both directions; thus, the major risk for them derives from extreme returns and market illiquidity. The authors examined the connection of the stress alarms of return- and liquidity-based measures to find an objective basis for stress measurement.

The authors defined two types of stress measures: indicators based on extreme returns and liquidity. It is suggested that the stress indicators should be based on the existing risk management methodology that examines different risk measure oversteps. The stress signals of the past nine years on the German stock market were analyzed. The authors investigated the connection between the chosen stress measures to obtain a robust measure for alarming stress.

Although extreme returns and illiquidity are both characteristics of stress, the correlation of returns- and liquidity-based stress indicators is low when taking daily values. On the other hand, the moving averages of the indicators correlate significantly in the case of measures of downward and upward extreme returns and liquidity measured by the relative spread. The results are robust enough to be used for monitoring stress periods.

This paper contributes to understanding the characteristics of stress periods and points to the fact that stress signals measured by different aspects can also differ within the same asset class. The moving averages of returns- and relative spread-based indicators, however, could provide a cost-effective quantitative support for the risk management of a CCP and make the margin calculation predictable for clearing members as well.

Liquidity is an important aspect of market efficiency. The purpose of this paper is threefold: first, this paper aims to discuss indicators that provide information about liquidity in agricultural land markets. Second, this paper aims to reflect on determinants of market liquidity and analyze the relationship with land prices. Third, this paper aims to conduct an empirical analysis for Germany that illustrates these concepts and allows hypothesis testing.

This study reviews liquidity dimensions and measurement in financial markets and derives indicators applicable to farmland markets. In an empirical analysis, this study exhibits the spatial and temporal variability of land market liquidity in Lower Saxony, a German federal state with the highest agricultural production value. This study uses a rich dataset that includes 72,547 sale transactions of arable land between 1990 and 2018. The research focuses on volume-based (number of transactions, volume and turnover) and time-based (trading frequency and durations) measures. A panel vector autoregression and Granger causality tests are applied to investigate the relation between land turnover and land prices.

The paper confirms the thinness of farmland markets but also reveals regional and temporal heterogeneity of land market liquidity. This study finds that the relation between market liquidity and prices is ambiguous. This study concludes that a high demand from expanding farms absorbs supply shocks regardless of the current price level in agricultural land markets.

Even though the relevance of agricultural land markets’ thinness is widely acknowledged in the literature, this paper is one of the first attempts to measure liquidity in agricultural land markets and to explain its relationship with land prices.

Purpose – This chapter compares the stability of the U.S. Dual Banking system's two bank groups, national and state banks, in light of the current financial crisis. The goal of the chapter is to answer three distinct questions: first, is there a difference in the (balance sheet) fragility between the two groups and, second, to what extent has the balance sheet fragility of both groups changed after the escalation of the financial crisis beginning in August 2007? Building on that, the third question asks to whether or not the respective regulatory agencies of both bank groups are responsible for these changes in balance sheet fragility in light of the financial crisis.

Methodology – To answer these questions the chapter uses U.S. Call Report data containing full quarterly balance sheets and P&Ls of all U.S. commercial banks over the period 2005–2008. Anecdotal evidence as well as univariate and multivariate difference-in-difference methodology focusing on the immediate pre-crisis period Q1/2005–Q3/2007 and the crisis period Q3/2007–Q4/2008 are applied.

Results – Highly significant and robust results show that, ceteris paribus, national banks reduced their potential balance sheet fragility after the escalation of the crisis in August 2007 by reducing lending and liquidity creation stronger than state banks. Anecdotal evidence supports the empirical findings. Although both FDIC and OCC did not anticipate the adverse effects of the crisis, the OCC publicly showed an earlier reaction to liquidity-related problems than the FDIC.

Originality – The chapter is the first of its kind to analyze bank fragility around the escalation of the financial crisis and the role of the regulatory agencies. The chapter holds especially interesting policy implications in the light of the current discussion about the future regulation of the banking markets.

This paper aims to focus on the absence of a direct criterion for the ideal level of net working capital (NWC) for which Acikgoz (2014) theoretically demonstrates that this NWC can be treated in a manner that allows the assessment of repayments. The study presents and discusses a new multiplier (i.e. the afa coefficient), defined as the ratio of cash equivalents ratio to NWC, measured as the percentage of short-term liabilities (Acikgoz, 2014). In other words, the study explores whether NWC could be an indicator of the ratios of corporate short-term bank credit to STL and of bank credit to total assets.

Sectoral panel regressions are used in the case of Turkey, spanning the period 1996-2013, on data obtained from the Central Bank of Turkey. Through second-generation panel unit root tests for cross-section dependence and panel cointegration methodologies, the results illustrate the statistical significance of the CD statistics, indicating the presence of cross dependence, the presence of non-stationary variables and the presence of a long-run association for the variables under study.

The findings document that a transformed variable of NWC is more substantive than the explicatory quality of the current ratio and may potentially be used in the prediction of bank credit in corporate liabilities.

The afa coefficient shows the ratio of liquid assets to NWC as a percentage of STL. The results illustrate that this coefficient plays a significant role for corporate bank credit usage in the case of the Turkish sectoral analysis.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

Since illiquidity risk is one of the most important pricing factors of assets, the aim of this paper is to evaluate the suitability of proxies of illiquidity prevalent in the asset pricing literature and their explanatory power in asset pricing tests.

Using the available high‐frequency intra‐day data, the paper constructs some proxies of illiquidity as benchmarks and then evaluates proxies of illiquidity based on inter‐day data.

The empirical results provide convincing evidence that turnover is the most suitable proxy of illiquidity in the Chinese stock market. It is not only hghly related to intra‐day data‐based proxies of illiquidity but also completely superior to other measures of illiquidity in asset pricing tests.

First, the paper applies illiquidity measurements from microstructure theory and the available high‐frequency data, and examines the suitability of illiquidity proxies in asset pricing literature in the Chinese stock market. Rational basics are provided to test the applicability of illiquidity measures in the Chinese stock market. Second, the paper introduces illiquidity proxies into asset pricing models to extend their explanatory power. The paper's results may help researchers to select illiquidity proxies more cautiously.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

This paper aims to identify and analyze the similarities and differences of the liquidity risk determinants within conventional and Islamic banks.

This study uses a dynamic panel data approach to examine the relationship between liquidity risk and a set of bank-specific and macroeconomic factors during 2005–2015, by selecting 27 Islamic banks and 49 conventional ones operating in the MENA region. More specifically, the dynamic two-step generalized method of moment estimator technique introduced by Arellano and Bond (1991) is applied.

The results suggest that the set of bank-specific variables influences the liquidity risk of both banking systems, while macroeconomic factors determine the liquidity risk of conventional banks. Islamic banks are not affected by macroeconomic determinants.

The research facilitates to the academicians, practitioners and bankers to have an alluded picture about liquidity risk determinants and their management. The findings can be used by bankers’ policy decision-makers to improve and enhance their consideration for liquidity risk management in both banking systems. Indeed, the study makes them aware to manage liquidity risk differently between conventional and Islamic banks, as the results reveal different liquidity risk determinants.

Compared to the abundant studies on the determinants of credit risk, researchers have not sufficiently addressed the factors influencing liquidity risk. Moreover, none of these few research studies has discussed and compared liquidity risk determinants within both banking systems operating in the Middle East and North Africa (MENA) region. This leads us to identify the similarities and differences between conventional and Islamic banks in the MENA region in respect of systematic and unsystematic determinants of the liquidity risk. The value is attributed to the increasing differentiation between Islamic and conventional banks. Islamic banks are characterized with a different liquidity structure distinguishing them from their conventional counterparts.

The purpose of this paper is (1) to determine the factors that significantly influence the capital structure, (2) to determine the factors that significantly influence profitability, (3) to find the factors that significantly influence growth opportunities, (4) to find reciprocal influence between capital structure and profitability and (5) to find reciprocal influence between capital structure and growth opportunity.

The population of this research is a manufacturing company listed on the Indonesia Stock Exchange during the period of 2010–2016. The number registered in the manufacturing sector is 144 companies. The sampling technique applied is purposive sampling. The fulfillment criteria are companies that have been approved before 2010. Another criterion is that the company is not delisting during the observation period. From that total of population, companies that meet the requirements are 117 companies. This observation was conducted for seven years since 2010–2016, so the center of the analysis of this research was a total of 819. The inferential statistics method used to analyze the research data is generalized structural component analysis (GSCA).

The results of this study indicate that (1) the factors that influence the capital structure include effective tax rate, financial flexibility, growth, uniqueness, asset Utilization, firm size and tangibility; (2) factors that affect profitability include liquidity, growth, firm age, uniqueness, tangibility, volatility, advertising and asset turnover; (3) growth opportunity have a negative and significant influence on capital structure. This means an increase in growth opportunity can be defined as an increase in depreciation that will not be used as collateral for managers to increase debt. This increase in debt will have an impact on reducing growth opportunities; (4) profitability and capital structure have a two-way causality relationship, which means they influence each other and (5) capital structure and growth opportunities have a negative reciprocal relationship.

The authenticity of the study is implied in the following explanation: The authors try to examine the reciprocal effect of capital structure on profitability and capital structure on growth opportunities and the factors that influence these two endogenous variables that have never been done by previous researchers. This research is motivated by research conducted by (Chathoth and Olsen, 2007; Jian-Shen Chen et al., 2009; Yang et al., 2010) using the structural equation model (SEM). However, this study uses GSCA as a method of research analysis.