Credit Default Swap Rates Research Articles

Behavioral finance impacts on US stock market volatility: an analysis of market anomalies

Abstract This study investigates the impacts of behavioral finance on stock market volatility. The primary aims are to explain the reasons behind changes in the S&P 500 price within the context of behavioral finance and to analyze investor behavior in response to these changes. To achieve this, the research employs time-series analysis over a 10-year period, focusing on the S&P 500, real interest rates, consumer confidence, market volatility and credit default swaps while considering the effects of behavioral biases. The findings reveal several significant correlations: rising real interest rates negatively affect stocks due to loss aversion and sentiment. Conversely, higher consumer confidence tends to positively influence the stock market, driven by herding behavior and optimism. Additionally, market volatility shows a negative correlation with the S&P 500, influenced by risk aversion, recency bias and herding behavior. Moreover, an increase in credit default swap rates leads to stock market declines, primarily influenced by risk perception, loss aversion and herding behavior.

Türkiye Stock Market in the Shadow of COVID-19 Pandemic: A QARDL Approach

This study examines the effects of the COVID-19 pandemic on the Türkiye stock market between March 14, 2020, and April 29, 2022, using the Quantile Autoregressive Distributed Lag (QARDL) model. The research investigates the relationship between the BIST100 index and selected economic indicators across quantiles ranging from 0.05 to 0.95. These indicators include daily new COVID-19 cases, the dollar exchange rate (FX), Brent crude oil prices (OIL), and credit default swap (CDS) rates. Findings indicate that an increase in COVID-19 cases has a negative impact on the stock market across various quantiles. While the dollar exchange rate generally shows a positive relationship with the BIST100 index, Brent crude oil prices, and CDS rates exhibit a negative impact. These results highlight the complex effects of both internal and external factors on the Türkiye stock market, providing significant insights for policymakers and future research.

AN ANALYSIS OF PAKISTAN'S DEFAULT RISK: CURRENT STATUS AND FUTURE IMPLICATIONS

This paper provides an analysis of Pakistan's default risk, including an overview of the current economic situation and factors contributing to the risk. The statistics and data on Pakistan's perceived default risk, such as credit-default swap (CDS) rates, are discussed. The analysis also includes an examination of Pakistan's ability to meet foreign debt obligations. The paper also examines past cases of default risk in Pakistan and possible solutions for mitigating future risks. The paper suggests that the country's default risk is currently high due to political instability, delays in IMF talks, weak foreign exchange reserves, and a lack of fiscal discipline and transparency in public spending. However, the paper also suggests that there are steps that can be taken to mitigate this risk, such as negotiating a bailout package with the IMF, implementing fiscal discipline and transparency measures, building stronger foreign exchange reserves, and encouraging foreign investment.

Covid-19, sovereign risk and monetary policy: Evidence from the European Monetary Union

This paper investigates the impact of Covid-19 pandemic and monetary policy measures adopted by the European Central Bank (ECB) on the sovereign risk for the European Monetary Union (EMU) countries for the period between March-2020 and November-2020 using daily data. The impact of Covid-19 and monetary policy shocks on the credit default swap rates and bond yields are investigated relying on a fixed effects panel regression model for five core (Germany, France, Austria, Netherlands, Belgium) and three periphery (Italy, Portugal and Spain) countries. To investigate the cross-country differences in responses, the interactions of the independent variables with periphery dummy and other country-specific variables are included in the regressions. The results of the empirical analysis suggest that Covid-19 shock increased the sovereign risk in the periphery EMU countries significantly and monetary policy measures have been effective in easing financial conditions in these countries. The results are insignificant for the core countries. The results also show that financial stability alleviates the negative impact of Covid-19 on the sovereign risk.

Time-frequency comovements between sovereign CDS and exchange rates: The role of sentiments

This study introduces various bivariate, multiple, and partial wavelet methods to explore the role of inclusive market sentiments in comovements between sovereign credit default swap (CDS) spreads and exchange rates from a time-frequency perspective. The empirical results show that the sovereign CDS spread and exchange rate nexus varies across countries and time-frequency domains. Further, the strong and positive comovements between sovereign CDS spreads and exchange rates are uncovered, concentered at medium- and long-term frequencies, especially in commodity-dependent countries. Among the various market sentiments, Volatility Index and Chicago Board Options Exchange Crude Oil Volatility are revealed as the most critical factors driving the comovements in commodity-dependent countries. The findings provide important recommendations for investors, regulatory authorities, and policymakers to understand the pivotal roles of market sentiments in inducing comovement between sovereign CDS spreads and exchange rates.

Turkish Stock Market from Pandemic to Russian Invasion, Evidence from Developed Machine Learning Algorithm

In recent time, the two significant events; Coronavirus epidemic and Russian invasion are effecting all over the world in various aspects; healthily, economically, environmentally, and socially, etc. The first event has brought uncertainties to the economic situation in most countries based on the epidemic transmission. In addition to that, on 24th February 2022 the Russian invasion of Ukraine affected negatively almost all stock markets all over the world, but the effects are heterogeneous across countries according to their economic-political relationship or neighbourhood, etc. Due to that, the stock market price in Turkey has been affected dramatically over that period. This empirical study is the first attempts to explore the impact of Coronavirus epidemic and Russian invasion on the stock market index XU100 in Turkey by applying the developed statistical method namely elastic-net regression based on empirical mode decomposition which can precisely tackle the nonstationary and nonlinearity data. Then we performed the robustness check by applying a nonlinear techniques Markov switching regression. The data are collected from the beginning of the epidemic in Turkey from March 11, 2020 until May 31, 2022. The finding reveals that there is significant effect of the Coronavirus spreading on the Turkish stock market index, particularly during the first wave. Then after the Russian Invasion the XU100 index is effected more negatively. As the credit default swap and TL reference interest rate have a negative impact but the foreigner exchange rate has a positive significant impact on the XU100 index, and it varies according to the period of short term and long term. Moreover, the results obtained by using the robustness check shows a robust and consistent finding. In conclusion, understanding the impact of Coronavirus pandemic and Russian invasion on the Turkish stock market can provide important implications for investors, financial sectors, and policymakers.

Covid-19 Pandemi Döneminde Makro Göstergelerin Borsa İstanbul Üzerindeki Etkileri

This research analyses the effects of the macro indicators like credit default swap, exchange rate, oil prices and gold prices on the Istanbul Stock Exchange (BIST100 Index) during the Covid-19 period by applying vector autoregressive model. In the model, daily data of the indicators are considered. The analysis comprises of two periods: pre-Covid-19 period (first week of 2019 to last week of 2020) and the during Covid-19 period (first week of 2020 to the second week of 2021). The comparison of two periods determines whether Covid-19 influence the impact of macro indicators on Borsa Istanbul. The findings reveal that in the pre-Covid-19 period, the volatility in BIST100 is explained mainly by gold prices. Credit default swap, oil prices and exchange rate affected BIST100 negatively, while gold prices had a positive impact on the Index. In the Covid-19 period, the impact of credit default swap and oil prices on BIST100 increased. A negative relationship is observed between BIST100 Index and the gold prices. A positive relationship is found between BIST100 and the exchange rate. Furthermore, the exchange rate had a greater impact on BIST100 than the impact observed in the pre-Covid period.

Covid-19'un Gelişmekte Olan Pay Piyasası Oynaklığına Etkisi: Borsa İstanbul'dan Ampirik Bulgular

The study aims to examine the impact of COVID-19 on the Turkish stock market volatility and reveal how different industries are affected by COVID-19. Volatility between pre-COVID and COVID periods are compared across industries to understand the impact of the first shock. Markov-switching dynamic regression (MSDR) model is employed to determine the transition from low volatility (pre-COVID) period to high volatility (COVID) period. The findings reveal a significant deterioration in volatility for all industries during the COVID-period, with a more dominant impact on the service sector. Then, factors that drive stock market volatility are investigated to understand the role of COVID-19 on increasing volatility. Results show that COVID-19 patients trigger volatility for all industries except food & beverages, insurance, non-metal mineral product, and wholesale & retail trade. On the other hand, an increase in the number of recoveries results in lower volatility for most of the industries. Besides, credit default swap increases volatility while the exchange rate lowers volatility. However, the magnitudes of credit default swap and exchange rate are greater than those of patients and recoveries, suggesting that COVID-19 is not the main driver of volatility for the Turkish stock market in the pandemic period.

Benchmark interest rates when the government is risky

Since the global financial crisis, interest rate swap rates, which represent future uncollateralized interbank borrowing, have fallen below maturity-matched Treasury rates. This is surprising, because US Treasuries, which are deemed expensive because of superior liquidity and safety, should produce yields that are lower than those of swap rates. We show, by no-arbitrage, that sovereign default risk explains negative swap spreads even without frictions such as balance sheet constraints, convenience yield, and hedging demand. We support this explanation with an equilibrium model that jointly accounts for macroeconomic fundamentals and the term structures of interest and US credit default swap rates.

The bank-sovereign nexus: Evidence from a non-bailout episode

Abstract We explore the interplay between sovereign and bank credit risk in a setting where Danish authorities first let two Danish banks default and then left the country’s largest bank, Danske Bank, to recapitalize privately. We find that the correlation between bank and sovereign credit default swap (CDS) rates changed with these events. Following the non-bailout events, the sensitivity to external shocks, proxied by CDS rates on the European banking sector, declined both for Danske Bank and for Danish sovereign debt. After Danske Bank’s recapitalization, its exposure to the European banking sector reappeared while that did not happen for Danish sovereign debt. The decoupling between CDS rates on sovereign and private bank debt indicates that the vicious feedback loop between bank and sovereign risk weakened after the non-bailout policies were introduced.

Internet search volumes of UK banks during the crisis: The role of banking structure and business model

This paper investigates the sensitivity of Google search volumes to the global financial crisis, using a large sample of UK banks. We find that abnormal volumes of searches on bank names are a timely indicator of credit risk, as measured by credit default swap rates, and are strongly related to crisis indicators. Search volumes for retail, private, and universal banks react more strongly than search volumes for other banks, suggesting that depositor concerns drive search volumes. Search volumes for internet banks and for European banks operating in the UK through branches are especially sensitive to crises.

CDS Proxy Construction via Machine Learning Techniques—Part I: Methodology and Results

To price and risk-manage over-the-counter derivatives, financial institutions have to estimate counterparty default risks based on liquidly quoted credit default swap (CDS) rates. For the vast majority of counterparties, liquid CDS quotes are not available and proxy CDS rates need to be constructed. Existing methods ignore counterparty-specific default risks and can lead to arbitrage. The authors propose to construct CDS proxy rates by machine learning techniques to associate liquidly quoted CDS proxy names to illiquid ones on the basis of observable financial feature variables. A benchmarking exercise shows that the proposed method leads to significantly smaller estimation errors than do existing methods. The authors tested 126 classifiers coming from the eight most popular algorithms, rank-ordered performance, and investigated performance variations among and within the classifiers. In Part I, the authors present the methodology, review the different machine learning techniques, and report on cross-classifier performance. In Part II, they focus on parametrization and intra-classifier performance, investigate correlation effects, and perform a benchmarking exercise. This is a first systematic study of CDS proxy construction by machine learning techniques and a first classifier comparison study entirely based on financial market data. The techniques should be of interest for financial institutions seeking proxies for CDS rates or other financial variables.

CDS Proxy Construction via Machine Learning Techniques—Part II: Parametrization, Correlation, Benchmarking

This is the second of two articles by the authors on the construction of credit default swap (CDS) proxy rates. In the first article, the authors proposed a machine learning (ML)-based proxy-rate construction technique that uses classification to construct so-called proxy names whose liquidly quoted CDS rates can be used as CDS proxy rates. The authors then compared the performances of ML classifiers from the eight most popular classifier families as a function of carefully selected sets of feature variables. In this second article, the authors take a closer look at the performances of the individual classifiers as a function of their different parametrizations, which they refer to as an intra-classifier performance study. The authors also examine the effects of feature variable correlations on classifier performance and perform a benchmarking exercise by comparing the ML-based CDS-proxy technique with two currently used proxy-rate construction methods: curve mapping and cross-sectional regression.

US Bank Credit Spreads During the Financial Crisis

This paper argues that first passage time models are likely to better than affine hazard rate models in modelling stressed credit markets and confirms their superior performance in explaining the behavior of Credit Default Swap rates for the major US banking groups over the period of the financial crisis. Affine models find it hard to deal with periods of exceptionally high or low default risk given their assumption of a constant rate of mean reversion in the hazard rate. In contrast, first passage time models are specified in terms of the distance to default rather than the hazard rate. The persistence of shocks varies with the distance to default, allowing the default curve to invert sharply (compress) when the distance to default is low (high). I use an empirical version of the Collin-Dufresne et al. (2003) model, which contains a smoothing parameter that allows it to control the relative effect of these shocks on the short spreads and can be interpreted as an information lag.

Market Credit Risk in Europe

The run-up to the Greek default featured marked increases in the cost of insuring sovereign debt from almost all European countries, as evidenced by their credit default swap (CDS) rates. One explanation for the perceived higher default risk in non-periphery countries is that market participants believed a default in the periphery might increase the risk of a future default in the core. To test for such dynamic contagion between credit related events in differing countries, we develop a procedure for tractably estimating high-dimensional Hawkes models using CDS prices. We escape the curse of dimensionality thanks to modelling the market portfolio of risk across countries, which serves as a sort of common factor. We further reduce dimensionality by taking a maximum-likelihood approach to estimation, avoiding having event intensities in the parameter set. Our approximation to the likelihood converges to the true likelihood as the sampling frequency goes to infinity; this ensures consistency even given non-stationarity, and permits tight identification. We find little evidence of shocks to one European country having an instantaneous effect on others, but risk in one country does gradually pull up risk in others.

Earnings surprises and the response of CDS markets

Purpose This paper aims to investigate the informational content of earnings surprises and accounting information in credit default swap (CDS) markets. Design/methodology/approach This paper analyzes a sample of 444 US firms and 6,907 earnings announcements. By means of parametric and non-parametric event study analysis, the paper assesses the informational value and the timeliness in the assimilation of earnings surprises by CDS rates. Findings This paper shows that earnings surprises contain material information and that CDS rates are affected by the disclosure of obligors’ financial statements. There is also supporting evidence that positive and negative surprises induce asymmetric reactions on CDS rates, especially after accounting for the credit risk of the obligor and the liquidity of the CDS contract. Finally, and perhaps the most interesting conclusion of the study, there is evidence that earnings disclosed during unstable periods lack informational value, in opposition to normal periods. Originality/value As compared with similar studies, this paper presents three novel contributions. The first concerns the use of non-parametric analysis in parallel with parametric tests to achieve robust conclusions. The second novel contribution resides in assessing whether the liquidity of the CDS contracts affects the information value of earnings surprises or the timeliness at which the information is assimilated into CDS rates. Finally, this paper also contributes to improve our understanding on the relationship between the business cycle and the informativeness of accounting information.

US bank credit spreads during the financial crisis

This paper argues that first passage time models are likely to better than affine hazard rate models in modelling stressed credit markets and confirms their superior performance in explaining the behavior of Credit Default Swap rates for the major US banking groups over the period of the financial crisis. Affine models find it hard to deal with periods of exceptionally high or low default risk given their assumption of a constant rate of mean reversion in the hazard rate. In contrast, first passage time models are specified in terms of the distance to default rather than the hazard rate. The persistence of shocks varies with the distance to default, allowing the default curve to invert sharply (compress) when the distance to default is low (high). I use an empirical version of the Collin-Dufresne et al. (2003) model, which contains a smoothing parameter that allows it to control the relative effect of these shocks on the short spreads and can be interpreted as an information lag.

Common-wide information flow in CDS markets: The case of earnings surprises of industry leaders

ABSTRACTThis paper examines the way common-wide information is disseminated in credit default swap (CDS) markets. I find strong supporting evidence that earnings announcements of CDS references with greater liquidity and higher credit risk help in predicting the subsequent CDS spread movements of their industry peers. This is consistent with the notion that the earnings announcements of certain firms may contain valuable market- and industry-wide information, which may be utilised afterwards to appraise the financial situation and the creditworthiness of industry peers, thereby influencing their CDS rates. Another major finding is that this common-wide information seems to be impounded with a lag into the CDS rates of industry peers. That pattern appears to be more pronounced for negative than for positive earnings surprises. The lag in the response to earnings surprises is higher among firms of the financial, cyclical consumer and energy sectors, and almost non-existent for firms of the sectors of technology and utilities. Attention costs and market frictions seem to fuel the pattern of under-reaction of CDS spreads to these earnings announcements.

Trading strategies with implied forward credit default swap spreads

Credit default risk for an obligor can be hedged with either a credit default swap (CDS) or a constant maturity credit default swap (CMCDS). We find strong evidence of persistent differences in the hedging cost associated with the two comparable contracts. Between 2001 and 2006, it would have been more profitable to sell CDS and buy CMCDS while after the crisis between 2008 and 2013 the opposite strategy was profitable. Panel data tests indicate that for our sample period the implied forward CDS rates are unbiased estimates of future spot CDS rates. The changes in the company implied volatility is the main determinant of trading inefficiencies, followed by the changes in GDP and in the interest rates before the crisis, and the changes in sentiment index and in the VIX after the crisis.

Wrong-Way Risk Correlation Coefficient Calibration

Under the new Basel III banking regulation banks should include wrong-way risk (WWR) into the calculation of the credit valuation adjustment (CVA) of the OTC derivatives. WWR takes place when the exposure to a counterparty is adversely correlated with the credit quality of that counterparty. Assuming a link between the interest rate swap (IRS), i.e. financial derivative in which two counterparties repeatedly exchange cash flows based on interest rate value and that the default time is represented by a Gaussian copula with a constant correlation coefficient, the WWR is expressed by this correlation coefficient. Because the observation of the default time means bankruptcy of the company, the correlation cannot be simply estimated using the observed data in contrast to the credit default swap (CDS) rate which is related to the intensity of default. Based on available daily Czech Republic government IRS and CDS rates we estimated the correlation using maximum likelihood method assuming that the systematic factor is governed by the AR(1) process, so we can decorrelate (eliminate autocorrelation) both time series. The results show that the correlation calibrated on the daily data is relatively high, and therefore the WWR should not be neglected in this case.