Financial Spreads Research Articles

Investigating Financial Risk Behavior Prediction Using Deep Learning and Big Data

This paper introduces a sophisticated deep learning model designed to predict high-risk behaviors in financial traders by analyzing vast amounts of transaction data. The model begins with an unsupervised pre-training phase, learning distributed representations that capture complex data relationships autonomously. It then utilizes a deep neural network, enhanced through supervised learning, to classify and predict traders' risk levels effectively. We specifically focus on financial spread trading related to Contracts For Difference (CFD), identifying potential misuse of insider information and assessing the risks it poses to market makers. By distinguishing between high-risk (A-book) and lower-risk (B-book) clients, the model supports strategic hedging decisions, crucial for market stability. Our extensive evaluations confirm the model's robustness and accuracy, highlighting its significant potential for practical implementation in dynamic and speculative financial markets where past trading performance may not predict future outcomes. This advancement not only refines risk management strategies but also contributes broadly to the domain of financial technology.

Impact of financial contagion on developed and emerging economies in the wake of the covid-19 pandemic and the Russia-Ukraine conflict

The world economy has been experiencing two double difficulties, including the COVID-19 pandemic and the war between Russia and Ukraine. The Russia-Ukraine war, accompanied by strong Western economic sanctions as well as Russia’s reactions, has had a comprehensive and profound negative impact on the world economy. With the openness of the economy as high as it is today, the influence between different economies is understandable. The research results focus on analyzing the financial contagion between mature and emerging markets in the post-COVID pandemic and the war of Russia and Ukraine. The study collected random stock index data from 5 developed and 5 developing countries from 2015 to the end of January 2023. The study uses the GARCH(1,1)-M model to find the financial spread between selected countries. Research results show that developed countries have a larger financial spread than developing countries, especially during the post-COVID pandemic and the war of Russia - Ukraine period.

Role of banking sector in green economic growth: empirical evidence from South Asian economies

The COVID-19 pandemic has slowed progress to the achievement of net-zero and sustainability goals. In particular, emerging economies may benefit greatly from the cooperation of banking institutions in promoting green recovery. This study focusses on banking institutions in South Asian countries that boost the intermediary financial spread, according to a thorough sample of banks from 2011 to 2021. The analysis employs the data envelopment analysis method, and the results are robust. In addition to these characteristics, we also consider aspects such as urbanisation, industrialisation, and population expansion. Banks may play a significant role in facilitating the realisation of environmental targets because of the clear advantages of the results, which provide comfort for green recovery. As green financing may lead to more efficient and robust financial systems, the results provide strong evidence for policymakers, financial institutions, and the financial sector.

Macroeconomic forecasting in times of crises

SummaryWe propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear, time‐varying, and combination forecasts for the period 1999–2015 and particularly in the Great Recession. When adding financial spreads, our method delivers further improvements for labor market variables and capacity utilization.

Global financial cycle, commodity terms of trade and financial spreads in emerging markets and developing economies

We examine how negative shocks which affect global financial conditions spill over into EMDEs through a fall in commodity terms of trade and greater financial spread. We estimate a two-equation dynamic panel data model for 33 of these economies during 1999.Q1-2020.Q4 to identify the effect of changes in the global financial conditions on the terms of trade and financial spread of commodities in these countries. In the first round, we focus on the diffusion of external negative financial spillovers from the financial center (the US) to the financial periphery, and to terms of trade and financial spread of commodities, leaving aside the feedback which operates from the first variable to the second one. In the second round, we allow commodities terms of trade to exert feedback on financial spreads. We find that the sign of this relationship depends on the productive structure and external trade pattern but differs from that of commodity net exporters and commodity net importers.

Estimation of short‐run predictive factor for US growth using state employment data

We estimate a predictive single factor model targeted to unobserved common growth in gross domestic product and gross domestic income (GDI) using a state-space framework with select state employment data. We use likelihood-based comparison to select the states to estimate the dynamic factor. The results show improved in-sample and out-of-sample performance than threshold principal component factors and financial spreads. Out-of-sample evaluations indicate larger gains for GDI growth with 14% to 20% lower mean squared forecast errors than other alternatives. Sectoral employment factors based on selected sectors using the state-space framework also show forecasting gains. An expanded model using both sectoral and state employment data shows that their common component is the primary predictive factor.

The Roles of Information Diffusion on Financial Risk Spreading on Two-Layer Networks

The financial risk spreads widely on the financial network and the risk information diffuses broadly on the social networks. How the information diffusion affects the financial risk spreading still lacks mathematical study. This paper proposes a model to describe the coevolution of financial information diffusion and financial risk spreading on a two-layered network. We use a mean-field approach to describe the spreading dynamics and adopt extensive numerical simulations to simulate the dynamics. We find that financial information diffusion can suppress the financial risk generally. Notably, there exists an optimal information diffusion probability at which the financial risk is greatly suppressed. Our results may shed some light on controlling the financial risk spreading dynamics.

Global Financial Cycle, Commodity Terms of Trade and Financial Spreads in Emerging Markets and Developing Economies

Global Financial Cycle, Commodity Terms of Trade and Financial Spreads in Emerging Markets and Developing Economies

Financial structure gap and bank lending-deposit rate spread in Sub-Saharan Africa: a panel analysis

ABSTRACT Sub-Saharan Africa (SSA) has one of the highest lending-deposit spreads. This study addresses a research gap by examining whether financial market structure explains the sub-continent’s high spread. The paper develops an original theoretical model. This is subsequently examined empirically. In and of itself, the study finds that financial market structure has no effect on the spread. However, it has a positive impact when it interacts with the differential between bank and stock market returns. Similarly, banking-sector concentration and the credit-deposit quotient increase the spread. In contrast, the following have a negative influence on the spread, namely: lagged degree of credit market equilibrium, government size and savings rate.

Interest On Reserves As Main Monetary Policy Tool

This paper examines the potential role of the interest on reserves as a main monetary policy tool, in a model of financial intermediation with financial and nominal frictions calibrated to US data (1985-2018). The interest on reserves is shown to affect financial spreads and real economic activity, through its effect on the banking system's reserves (balance sheet channel) and the price of safe liquid assets (intertemporal Euler equation channel). It is shown to provide, (i) determinacy when the interest rate is fixed to zero, through the intertemporal Euler equation channel, independently of the reserves-to-deposits ratio, or bank capital restrictions; (ii) similar welfare improvements to an optimal Taylor rule; (iii) an alternative main monetary policy tool for driving the economy out of recessions when the conventional interest rate is trapped at the zero-lower-bound.

The impact of trade war on Shanghai stock exchange industry based on Granger causality network

<sec>The impact of the China-US trade war on the industry is a common concern. Industries in the stock market have a high degree of correlation that the drastic fluctuation of stock prices of one industry may cause related industry stock price fluctuating, and even may influence the whole financial market through chain reaction. Therefore, it is helpful for us to understand the impact of the China-Us trade war on Shanghai stock market and the internal relations among the different industry sectors by analyzing how the financial shock spreads in the stock market.</sec><sec>However, there are still several essential problems to be solved. First, previous work mainly employed the mean field theory to study the diffusion of financial crisis in the stock market. Although this method can reflect the diffusion of financial crisis in the stock market as a whole, it is not accurate enough to explain the relationship among industry sectors. Second, the previous work mainly used numerical simulations to study the dynamic properties of the spread model, thus there is necessity to demonstrate whether numerical simulations can reflect the real situation of stock market.</sec><sec>To solve these two problems, this paper proposes a method combining parameter estimation techniques and the Monte Carlo simulation algorithm based on the disease spreading model. By using this method, we select the Shanghai stock exchange industry indexes from 2016 to 2019, construct the Granger causality network, estimate the parameters of the risk spreading model using the event study methodology, and finally simulate the diffusion of financial shocks. The results show that: firstly, the trade war has significantly changed the structure of Shanghai stock exchange industries, and industry indexes have become more closely related. Secondly, after the trade war, the financial shock will cause the number of infected nodes in Shanghai stock market increasing rapidly in the initial stage, and the scale of infection will reach the peak within the 10th to 15th trading days. The number of susceptible infections begins to slow down on about the 25th trading day, which means that the infection caused by financial shock has ended and the market is gradually recovering. Thirdly, the calculation results of the basic regeneration number show that the risk caused by financial shock is easy to spread in the Shanghai stock market after the trade war, and the phenomenon of "simultaneously rise and fall" of Shanghai stock market easily emerges.</sec>

Dynamic responses of real output to financial spreads

In this paper, we find that a positive term spread shock permanently increases real output in that the long-term trend rises. The mechanism is likely through the strong association between term spread shocks and total factor productivity news shocks. A positive credit spread shock could reflect heightening default risk and tightening credit supply, and both factors can lead to fewer new investment projects and damage the long-run economic capacity. We find that for credit spread shocks representing tightening credit supply conditions, the permanent effect is significant in a counterfactual case in which the public forecasts future short rates without considering credit spread. This finding suggests that the central bank's commitment to provide liquidity is important to prevent permanent output loss due to a shortage of credit.

Do Energy and Banking CDS Sector Spreads Reflect Financial Risks and Economic Policy Uncertainty? A Time-Scale Decomposition Approach

The aim of this study is to investigate the dynamics of the co-movements of energy and banking sector credit default swaps (CDS) spreads with global financial and economic policy uncertainty and risk factors in the United States and Europe. We first employ the standard quantile regression approach to examine the co-movement dynamics under different credit market conditions. The empirical results show that the standard quantile regression fails to capture the co-movement between the U.S. and European CDS spreads and the economic policy uncertainty and risk factors for all quantiles. However, after decomposing the raw series at various scales of resolution, using the wavelet approach to capture the structure, we find that the financial and energy related CDS spreads co-move with the economic policy uncertainty and stock market risk in the intermediate and lower frequency domain. That is, there is a dependency in the intermediate and higher time scales or the intermediate and long investment horizons for both the United States and Europe. However, there are positive effects of the stock market risk index on the U.S. banking CDS spreads, which remain almost similar across quantiles, but the effects are asymmetric for the European banking spreads. The study also provides policy implications.

The Spillover and Transmission of Chinese Financial Markets Risk

After the 2008 financial crisis, the whole world financial markets became more fluctuates, the same to China also. It is necessary to pay great attention to high volatility problem in Chinese market, and also the uncertainty problem, risk accumulation and spillover effect come along with it. This paper calculates stock market return and builds financial stress index to explore the risk spillover effect. Empirical results show that the Chinese financial market have higher volatility than other countries. The Chinese stock market had higher dynamic market co-movement with international financial markets after 2008 financial crisis. What’s more, this article also finds the financial risk spreads between China and US. When the US financial stress index increases, China's financial stress index experiences a larger increase. However, after the change in China's financial stress index, the US financial stress index has no obvious trend of change. So we should pay more attention to periods of Chinese financial market risk and its spillover.

Determinantes del spread de los bonos corporativos de compañías estatales. El caso de CODELCO

This article aims to measure the impact on CODELCO debt financial spreads as a result of the changes in various factors, mainly resulting from the fall in copper prices. In this sense, the article addresses a public policy issue in todays Chile, because of the low copper prices, that have reduced the company’s contributions to the treasury. The analysis is performed with a sample of data that is quarterly arranged and range from 2003 to 2015, and the goal is to measure the debt spread determinants from a credit risk perspective. As expected, the relationship between the average maturity of the bonds and the spread is inverse, the probability of ”default” and the bond spread relate positively and the price of copper is related negatively with the bond spread. Contrary to what is expected risk classification is not statistically significant and the cash to total assets is positively related with the bond spread.

The Determinant of Financial Statements-Content Information: Corporate Governance as a Mediating Variable

<p>This study aimed to examine the effect of human resource capacity, internal control, utilization of information technology and corporate governance of the financial statement information. Retrieving data using a survey method. The sample in this study is the head of the bureau, division chief, section chief, head of the sub-division and the administrative manager of financial spread in the working area of Perum Jasa Tirta I is Brantas River and Bengawan Solo. A total of 148 data processed using smartPLS. The results of this study indicate that the capacity of human resources and internal control affecting corporate governance, in addition to the capacity of human resources and corporate governance affects the financial statements information directly. The implication of this study is the management of Perum Jasa Tirta I must ensure that the human resources that exist in the company are resources that are competent and have sufficient internal control, so as to improve the implementation of corporate governance and financial reporting information delivered in a timely manner.</p><p><br /> <strong>Keywords</strong>: determinants of financial statements information, corporate governance.</p>

Dynamic Responses of Real Output to Financial Spreads

This paper investigates how the term spread and the credit spread affect real output in the long-run. Predictive regression estimates imply that high term spreads signal long-lasting increases in output, while high credit spreads signal brief economic contraction. However, an impulse response analysis indicates that a positive shock in the credit spread leads to higher term spreads possibly due to monetary policy reactions. This interaction between term and credit spreads may lead to the inability of the credit spread to signal long-lasting changes in output. I thus specify a Vector Autoregression model and conduct a counterfactual analysis, which shuts down the interaction between spreads. The results using US data are summarized as follows: (i) a positive term spread shock, due to a TFP news shock, rises real output permanently; (ii) without the induced changes in the term spread, a positive credit spread shock causes the trend output to decline because of tight credit supply condition.

Evaluating monetary policy with financial stability objective: rules vs. discretion

ABSTRACTThis article explores whether adding the goal of financial stability to the more traditional goals of output and price stability could improve optimality of monetary policy. A Dynamic Stochastic General Equilibrium model that endogenously incorporates financial frictions is used to derive optimality conditions across rule-based and discretionary monetary policy environments. The results indicate that it is optimal for the Central Bank to keep output below the potential level in the short term so as to dampen the inflationary effects arising from supply and financial shocks. When the economy is exposed to a financial shock, both leverage and credit spread rise significantly, thereby tipping the economy into a financial crisis and raising the probability of macroeconomic risk.

Financial Shocks and Optimal Monetary Policy Rules

We assess the performance of optimal Taylor-type interest rate rules, with and without reaction to financial variables, in stabilizing the macroeconomy following financial shocks. We use a DSGE model that comprises both a loan and a bond market, which best suits the contemporary structure of the U.S. financial system and allows for a wide set of financial shocks and transmission mechanisms. Overall, we find that targeting financial stability ‐ in particular credit growth, but in some cases also financial spreads and asset prices ‐ improves macroeconomic stabilization. The specific policy implications depend on the policy regime, and on the origin and the persistence of the financial shock.

Monetary policy and the trade weighted dollar

PurposeThe purpose of this paper is to investigate the performance of the trade‐weighted US dollar from 1973 to 2011 as a result of monetary policy.Design/methodology/approachRelevant time series variables – the money supply, the federal funds rate, general financial conditions, national income and interest rate spread are used to investigate the impact of shocks on the US trade‐weighted dollar and to explain the predictive power the variables hold over the weighted dollar. This is accomplished by using the conventional procedures of variance decomposition and Granger causality tests.FindingsThe paper finds that unexpected changes in national financial conditions, the federal funds rate and the velocity of money account for more variation in the performance of the trade‐weighted US dollar than do surprises associated with the interest rate spread (the variable that tracks quantitative easing (QE), quantitative contraction (QC) and neutrality).Practical implicationsThis article is unique in adding to the literary discourse by incorporating international trade and other national conditions as key indicators of the long‐term value of a trade‐weighted currency and its propensity to increase national income. It provides an opportunity for further analysis of the role of QE in currency valuation when the short‐term interest rate becomes an inadequate monetary policy instrument for economic stabilization and determining the value of a currency.Originality/valueThe paper argues that the velocity of money has strong predictive power over the performance of the trade‐weighted dollar and that monetary policy can help to predict changes in the financial and real sectors, but not the value of the trade‐weighted dollar directly or in isolation. This is partly because the monetary policy transmission mechanism and external prices are also relevant to the weighted value of the currency over an extended period of time.