J. Risk Financial Manag., Volume 13, Issue 4 (April 2020) – 23 articles

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures. Full article

A considerable number of studies have examined the relationship between global prices and local prices in food-importing nations, but the linkages between international prices and the producer prices of large agricultural exporters have been largely ignored. This paper analyzes the connections between world prices and U.S. producer prices in the wheat, soybeans, and corn markets using a vector error correction generalized autoregressive conditional heteroscedastic model with a multivariate Baba-Engle-Kraft Kroner specification (VECM-GARCH-BEKK) and cross-correlation function (CCF). Our findings indicate firstly that a long-run equilibrium relationship exists between international and U.S. producer prices for the three agricultural crops. It also finds a significant bidirectional causality-in-mean and causality-in-variance between international and U.S. producer prices for these crops. Finally, the empirical results suggest that international wheat and corn prices play a leading role in U.S. local markets in return transmissions and that U.S. wheat price can be considered to be a leading indicator of the global wheat price in volatility transmissions. Full article

Interest rates are representative indicators that reflect the degree of economic activity. The yield curve, which combines government bond interest rates by maturity, fluctuates to reflect various macroeconomic factors. Central bank monetary policy is one of the significant factors influencing interest rate markets. Generally, when the economy slows down, the central bank tries to stimulate the economy by lowering the policy rate to establish an environment in which companies and individuals can easily raise funds. In Japan, the shape of the yield curve has changed significantly in recent years following major changes in monetary policy. Therefore, an increasing need exists for a model that can flexibly respond to the various shapes of yield curves. In this research, we construct a three-factor model to represent the Japanese yield curve using the machine learning approach of an autoencoder. In addition, we focus on the model parameters of the intermediate layer of the neural network that constitute the autoencoder and confirm that the three automatically generated factors represent the “Level,” “Curvature,” and “Slope” of the yield curve. Furthermore, we develop a long–short strategy for Japanese government bonds by setting their valuation with the autoencoder, and we confirm good performance compared with the trend-follow investment strategy. Full article

Crowdfunding (CF) has experienced impressive growth in recent years with the development of internet and information technologies that increased the participation of the “crowd” to fund entrepreneurial projects. Young entrepreneurs, especially well-qualified students, have recently begun to play a new role in the economy by launching new ventures in niche markets. The aim of the present paper is to provide a deeper understanding of CF among Portuguese young potential entrepreneurs as an alternative funding mechanism, by discussing its main characteristics and the perceived benefits and barriers that might drive young entrepreneurs to post a project on a CF platform or discourage its use. Through an online survey, we query well-qualified students about the knowledge they have about crowdfunding and benefits and barriers that can increase or reduce the possibility of funding to launch a new venture. The results show that potential young entrepreneurs have moderate knowledge about CF. Consequently, they are not able to explore all the business models available, specifically the models related to investment (lending and equity). The respondents perceive several benefits of the use of CF that go beyond the financial advantages, such as the communication of the project to a wider audience and the additional feedback from potential customers. The perceived barriers that could deter the use of CF are related to the implementation of the CF campaign, although contextual constraints have been mentioned. Full article

Crowdfunding allows the public to donate small amounts of money to entrepreneurs through online platforms. In contrast with traditional financial institutions, this new method facilitates the financing process through direct and easy online contact between initiators and investors. Based on the data obtained from Kickstarter, the largest crowdfunding platform, we investigate 27,117 crowdfunding projects from 1 January 2015, to 30 June 2015, and we find that a crowdfunding campaign with a realistic funding goal, a suitable funding period, and more updates and interactions with investors is much more likely to be successfully funded. In addition, the different types of founders are very influential in crowdfunding outcomes. For example, females tend to be more successful than males at collecting funds. Founders in the form of teams, companies, or a specific project are also beneficial to funding outcomes. Full article

The uncertainty in the financial market, whether the US—China trade war will slow down the global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets. In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model that uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events. The correlation was higher between the volatility of the market and uncertainty indices with larger expected supervised signal compared to uncertainty indices with the smaller expected supervised signal. We also applied the impulse response function to analyze the impact of the uncertainty indices on financial markets. Full article

We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory (LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low latency. In a real-world example, we show that an increased model size may compensate for a higher latency. As the noisy nature of economic trends complicates predictions, especially in speculative assets, our approach does not predict courses but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a virtual market environment, based on historical trading data. Our environment provides a partially observable Markov decision process (POMDP) to reinforcement learners and allows the training of various strategies. Full article

There is no doubt about the importance of diagnostic testing in an emergency; specifically, which range of tests is available, where and when they are dispensed, and who might be tested using laboratory-developed tests, or other diagnostic tests including experimental tests. This includes testing for the SARS-CoV-2 virus that causes the COVID-19 disease. Testing is essential to “flatten the curve” of the number of confirmed positive cases of the disease, in addition to handwashing, isolation, and social distancing, among other essential measures. Is one diagnostic test enough to obtain the correct decision about a confirmed positive outcome? Full article

Bridging global economic inequalities calls for effective financial alternatives such as awqāf banks to better attend to the needs of the poor and underprivileged. This is expected to address the root causes of poverty and ensuing economic gaps, improving much of the living standards whether pertaining to education, health, shelter, employment or basic social services while reducing the state’s economic and financial burden. We envision awqāf banks as institutions which are established through cash awqāf and which operate multiple awqāf funds alongside an assortment of financial instruments. The main use of their awqāf funds are the issue of low-cost credit to the poor, economically disadvantaged and underprivileged, instead of focusing solely on generating and maximizing shareholder profits. This is to support the economy through of steady and sustainable growth, effectively raising the lower bar on per capita income and lifting multitudes out of poverty and need. This paper explores how low-cost credit can be provided to the poor or lower income demographics through awqāf banks, while addressing relevant issues such as Shari’ah compliance, services rendering, investment and awqāf distribution. This paper also examines current studies on awqāf in relation to finance and banking, the basic functions, and characteristics of the Shari’ah-compliant awqāf bank, as well as evaluations of awqāf banks. Current studies show that there is a legitimate need for Shari’ah-compliant awqāf banks which not only providing services for its beneficiaries but also manage investments and awqāf funds that contribute to overall national development and economic growth. This study would be of high relevance to experts, practitioners, financial managers, regulators, and policy makers in the fields of awqāf, banking and finance. Full article

In this study, we assessed the impact of capital adequacy ratio (CAR) regulation in the Basel regulatory framework. This regulation was established to make the banking network robust. However, a previous work argued that CAR regulation has a destabilization effect on financial markets. To assess impacts such as destabilizing effects, we conducted simulations of an artificial market, one of the computer simulations imitating real financial markets. In the simulation, we proposed and used a new model with continuous double auction markets, stylized trading agents, and two kinds of portfolio trading agents. Both portfolio trading agents had trading strategies incorporating Markowitz’s portfolio optimization. Additionally, one type of portfolio trading agent was under regulation. From the simulations, we found that portfolio optimization as each trader’s strategy stabilizes markets, and CAR regulation destabilizes markets in various aspects. These results show that CAR regulation can have negative effects on asset markets. As future work, we should confirm these effects empirically and consider how to balance between both positive and negative aspects of CAR regulation. Full article

In 2010, the Basel Committee on Banking Supervision published an assessment of the long-term economic impact (LEI) of stronger capital and liquidity requirements. This paper considers this assessment in light of estimates from later studies of the macroeconomic benefits and costs of higher capital requirements. Consistent with the Basel Committee’s original assessment, this paper finds that the net macroeconomic benefits of capital requirements are positive over a wide range of capital levels. Under certain assumptions, the literature finds that the net benefits of higher capital requirements may have been understated in the original committee assessment. Put differently, the range of estimates for the theoretically optimal level of capital requirements—where marginal benefits equal marginal costs—is likely either similar to, or higher than was originally estimated by the Basel Committee. The above conclusion is however subject to a number of important considerations. First, estimates of optimal capital are sensitive to a number of assumptions and design choices. For example, the literature differs in judgments made about the permanence of crisis effects as well as assumptions about the efficacy of post crisis reforms, such as liquidity regulations and bank resolution regimes, in reducing the probability and costs of future banking crisis. In some cases, these judgements can offset the upward tendency in the range of optimal capital. Second, differences in (net) benefit estimates can reflect different conditioning assumptions such as starting levels of capital or default thresholds (the capital ratio at which firms are assumed to fail) when estimating the impact of capital in reducing crisis probabilities. Finally, the estimates are based on capital ratios that are measured in different units. For example, some studies provide optimal capital estimates in risk-weighted ratios, others in leverage ratios. And, across the risk-weighted ratio estimates, the definition of capital and risk-weighted assets (RWAs) can also differ (e.g., tangible common equity (TCE) or Tier 1 or common equity tier 1 (CET1) capital; Basel II RWAs vs. Basel III measures of RWAs). A full standardisation of the different estimates across studies to allow for all of these considerations is not possible on the basis of the information available and lies beyond the scope of this paper. This paper also suggests a set of issues which warrant further monitoring and research. This includes the link between capital and the cost and probability of crises, accounting for the effects of liquidity regulations, resolution regimes and counter-cyclical capital buffers, and the impact of regulation on loan quantities. Full article

In this paper we analyze the effectiveness of more than 30 years of efforts by international banking supervisors, working together in the Basel Committee on Banking Supervision, to harmonize capital and liquidity standards for internationally active banks. Notwithstanding the great efforts and progress made by international banking supervisors since the financial crisis of 2007–2009, two important issues require further attention. First, although bank capital ratios have been raised significantly since the recent financial crisis, they are still at historically low levels. In a world in which global debt ratios have risen even further during the past decade, this is a worrying signal of fragility in the global financial system. Second, bank liquidity requirements may have become too complex and could also have unintented and unpredictable interaction effects with bank capital requirements. Full article

The impact of tax incentives on the investment attractiveness of bonds for retail investors is assessed in the article. The paper presents a comparative empirical analysis of investment attractiveness of Russian bonds and bank deposits for domestic retail investors. We identify investment preferences of retail investors in Russia, analyze investment characteristics of deposits in Russian banks and a variety of bonds available for retail investors. Given the tax benefits of the recently introduced Individual Investment Account, we show that the real yield of investment in government bonds is over eight times higher than the yield of bank deposits. Despite higher risks of investing in bonds, we conclude that government bonds taking into account the tax benefits of the Individual Investment Account could be a realistic alternative to bank deposits for Russian retail investors. Full article

Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile. Full article

We examine the impact of oil price and oil price volatility on US illiquidity premiums (return on illiquid-minus-liquid stocks), using the US Oil Fund options implied volatility OVX index. We use daily data from 2007 to 2018, taking into account the structural break in June 2009 and controlling for macroeconomic factors. Both OLS and VAR models indicate that oil price has a significantly positive impact and OVX has a significantly negative impact on premiums, for the full sample and post-crisis period. These relationships are potentially driven by investor sentiments and market liquidity. Oil price has a negative impact on premiums during the crisis period. Using an autoregressive distribution lag model and an error correction model, we analyse long- and short-run elasticities. We find that oil price has a significantly positive impact on premiums both in the long- and short-run, for the full sample and post-crisis period. OVX only has a significantly negative impact in the short-run for the full sample. The reverting mechanism to establish long-run equilibrium is effective for the full sample and post-crisis period. Illiquidity premiums do not show any asymmetric responses to oil price changes but we do find evidence of asymmetric response to OVX changes. Full article

This paper analyzes the conditional correlations between the stock market returns of countries that are members of the Gulf Cooperation Council (GCC). The innovative aspects of the paper consist of focusing on three volatility indices: the oil (OVX), gold (GVZ), and S&P500 (VIX) markets (considered in log-difference). We use weekly data and resort to DCC-GARCH modeling. The novelty of the paper consists in revealing that: (i) GCC stock market returns are negatively correlated with each of the volatility measures, and the correlations are stronger during crisis periods; (ii) GCC stock returns are mostly correlated with oil shocks; and (iii) Saudi Arabia and Qatar are the most responsive to all shocks among the GCC countries, while Bahrain correlates weakly to shocks in oil, gold, and VIX. The most striking results feature extra sensitivity of Saudi Arabia and Qatar in terms of volatility indices, which should be the foremost concern of policymakers and banking analysts. Full article

In this study, we focus on how banks can enhance their efficiency in the utilization of resources to ensure their economic sustainability. We propose a novel three-stage (production, investment, and revenue generation) network Data Envelopment Analysis (DEA) with bootstrapping to evaluate the performance of the six big Canadian banks for the period 2000–2017, amid the 2007 financial crisis and the increasing competition level due to new technologies. We identify the best practices in each stage that can be used as benchmarks by other banks to improve their economic sustainability. Our results indicate that the 2007 financial crisis resulted in lower efficiencies in the performance of Canadian banks. This decline was not substantial for the production and investment stages when the revenue generation stage received the greatest hit. In addition, we observed that the individual banks did not have consistent performance in the different stages. Finally, we compared our model with the black box DEA model and concluded that the network DEA provides more insightful and accurate results in terms of banks’ efficiencies. Full article

The research interest in bank profitability and efficiency is linked to the economic situation and an important issue for policymakers is to ensure economic stability. Nevertheless, managerial decisions and the environment could play a critical role in ensuring proper and efficient allocation of the resources. The purpose of this study is to understand which are the main factors that can influence the performance and efficiency of 94 commercial listed banks from Eurozone countries through a dynamic evaluation, in the period between 2011 and 2016. To achieve this aim, the generalized method of moments estimator technique is used to analyze the influence of some bank-specific characteristics, controlled by management, on the profitability as a measure of bank performance. After that, through the value-based data envelopment analysis (DEA) methodology, those factors are considered in determining the efficient banks. The results show that banking efficiency depends on set bank-specific characteristics and that the effect of determinants on efficiency differs, considering the macroeconomic conditions. Full article

This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development. Full article

The Nelson–Siegel framework published by Diebold and Li created an important benchmark and originated several works in the literature of forecasting the term structure of interest rates. However, these frameworks were built on the top of a parametric curve model that may lead to poor fitting for sensible term structure shapes affecting forecast results. We propose DCOBS with no-arbitrage restrictions, a dynamic constrained smoothing B-splines yield curve model. Even though DCOBS may provide more volatile forward curves than parametric models, they are still more accurate than those from Nelson–Siegel frameworks. DCOBS has been evaluated for ten years of US Daily Treasury Yield Curve Rates, and it is consistent with stylized facts of yield curves. DCOBS has great predictability power, especially in short and middle-term forecast, and has shown greater stability and lower root mean square errors than an Arbitrage-Free Nelson–Siegel model. Full article

The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a novel class of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. The inhomogeneity of the cross-sectional distribution of realized volatility is captured by a finite Gaussian mixture model plus a uniform component that represents abnormal variations in volatility. Based on the cross-sectional mixture model, at each time point, memberships of assets to risk groups are retrieved via maximum likelihood estimation, as well as the probability that an asset belongs to a specific risk group. The latter is profitably used for specifying a state-dependent model for volatility forecasting. We propose novel GARCH-type specifications the parameters of which act “clusterwise” conditional on past information on the volatility clusters. The empirical performance of the proposed models is assessed by means of an application to a panel of U.S. stocks traded on the NYSE. An extensive forecasting experiment shows that, when the main goal is to improve overall many univariate volatility forecasts, the method proposed in this paper has some advantages over the state-of-the-arts methods. Full article

Investors are interested in knowing whether sukuk bonds and shariah stock indices in the Gulf Corporation Council (GCC) region are related. This study examines the connectedness between the sukuk- and shariah-compliant stock indices in the GCC financial markets. Bivariate and multivariate wavelet approaches are applied to the daily data covering the period 10 July 2008 to 15 May 2017. The empirical findings demonstrate a strong correlation between these GCC sukuk bond indices and shariah stock indices. The degree of connectedness between these sukuk and shariah stock indices varies across time and scale. A strong and positive association is observed in the short term and a negative association is evident in the long term. The same findings are observed, using the wavelet cohesion approach that also validates the existence of portfolio diversification opportunities at a short-time horizon. The multivariate cross-correlation analysis reveals that these sukuk and shariah stock markets are highly integrated across time and scale. Furthermore, the value at risk (VaR) for the sukuk bond–shariah stocks portfolio is performed to highlight the significance of the wavelet analysis. The outcomes show that portfolio stocks are variable with respect to time or scale (time diversification). Overall, analyzing the sukuk bond–shariah stock index returns in the GCC at a multiscale level makes it easier for financial agents dealing with heterogeneous trading horizons to assess the benefits of diversifications. Full article

In eras of intense debates on the appointment of women on corporate boards, this research sheds light on the structure of board in Asian emerging economies by examining how women on board of family businesses separately and collectively affect the dividend announcement of business organizations. On the basis of the panel data of four Asian emerging economies—China, Malaysia, Pakistan, and India—for the period 2010–2018, the results from our Tobit regression showed the adverse (negative) and significant impact of women on boards and in family businesses upon dividend announcement. It is important that policymakers should not view firms with one eye. There should be a spillover on board gender diversity from international to domestic levels, and international firms should be set as an example for domestic firms for the inclusion of women on boards. It might be the best time for Asian emerging economies to take productive action for balancing the gender in boardroom settings, and to set a minimum mass of women on boards for better and more effective decision making. Full article