Credit Default Swap Prices Research Articles

Matrix GARCH model: Inference and application*

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the model identification. Moreover, we introduce a quasi-maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model, and establish its theoretical properties. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices.

The importance of green patents for CDS pricing: The role of environmental disclosures

Corporate green innovation to address global climate change has potential spillover effects through positive financial outcomes. This study examines how the credit market takes green innovation, measured by the approval of green patents, into account to determine the credit default swap (CDS) premia. Using a sample of United States (US) firms from 2002 to 2020, this research finds that the CDS market favourably prices green innovation: firms with more green patents have lower CDS spread, suggesting that the CDS market considers firms' green innovation in their investment decision. While greater firm-provided environmental disclosures boost the effect of green innovation, involuntary environmental exposures by external channels such as print and online media offset the favourable effect of green innovation on CDS spread. The green technology pilot program by the US Patent and Trademark Office in 2009 is exploited as a quasi-natural experimental setting to apply a difference-in-differences analysis, confirming the favourable effect of green innovation on CDS spread. The results remain robust to various sensitivity and other endogeneity tests. Managers intending to reconcile green innovation with their stakeholder value maximisation objective may find the findings interesting.

Credit default swap spreads modeling and forecasting with a stochastic square-root three-factor model

In this study, we consider the CIR3 model, a three-factor stochastic model with correlated trends and volatilities for modeling and forecasting credit default swap (CDS) spreads. After recalling existence and uniqueness results, together with a generalized Feller condition to ensure positivity, we use a Lamperti-type transform to rewrite our SDEs system in a form in which the stochastic part of the leading process is uncorrelated with those of its mean and volatility processes. Finally, we calibrate the model through the estimating function approach for ergodic diffusions and simulate the CDS prices by discretizing our (transformed) system. These findings contribute to a deeper understanding of stochastic models with correlated trends and volatilities, with applications in pricing, trading and risk assessment.

Credit default swap pricing with counterparty risk in a reduced form model with Hawkes process

Credit default swap pricing with counterparty risk in a reduced form model with Hawkes process

Regulation and the demand for credit default swaps in experimental bond markets

Credit default swaps (CDS) played an important role in the financial crisis of 2008 leading to calls for regulation. Here, we seek to understand the impact of a CDS regulation that restricts the possibility to hold naked CDS. We use a controlled laboratory experiment analyzing CDS pricing in a bond market subject to default risk. Our results show that the regulation achieves the goal of increasing the use of CDS for hedging purposes while reducing the use of CDS for speculation. This success does not come at the expense of lower initial public offering (IPO) prices for the bonds or worse pricing of bonds or CDS in the secondary market.

Corporate credit default swap systematic factors

AbstractWe examine a comprehensive set of systematic and firm‐specific determinants of the credit default swap (CDS), using a two‐step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average of 35%), while firm‐specific factors are limited (with of 5% in panel regression) with only 4 out of 28 firm‐specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management.

Research on Credit Default Swaps Pricing Considering Moral Hazard Incentive under Reduce-Form Model

Research on Credit Default Swaps Pricing Considering Moral Hazard Incentive under Reduce-Form Model

Pricing of Credit Default Swaps from the Perspective of Credit Enhancement in PPP Projects

The difficulty in financing has become the primary obstacle in the process of promoting a public–private partnership (PPP). Asset securitization is a financial model that can exchange the predictable cash flow of illiquid PPP project assets with the current cash flow, and credit enhancement is crucial in this process. Different from the traditional external credit enhancement of PPP projects based on government guarantees, this paper introduces an innovative credit risk management tool, i.e., credit default swaps (CDS), into the process of asset securitization in the PPP model. In order to avoid opportunistic behavior caused by information asymmetry in the process of financial product transaction, a CDS trading contract between PPP project investors and risk management institutions based on the principal–agent theory and a flexible CDS pricing model is proposed. Through a demonstration case of the Leshan to Rongjiang Expressway in China, the volatility correlation among CDS contract default probability, pricing, and related factors, such as default limits, operation period, compensation amount, project investment return, investment return volatility, and PPP project investors’ expected inputs, is obtained. The study shows that (1) appropriate CDS pricing helps to generate investors’ high expected inputs and diligence with controls; (2) there is a positive correlation between the change of default limit and compensation amount, and the set value of default limit should not be too high, otherwise it may induce the increase of default probability; and (3) based on the high return on assets and moderate asset volatility of PPP projects, the probability of default of CDS contracts is low. The innovative financial mechanism developed by the principal–agent theory could enrich the credit enhancement methods in asset securities of PPP projects.

Indifference pricing of credit default swaps in a multi-period model

Credit default swaps (CDS) are the most common type of credit derivatives used for hedging and speculative purposes in credit markets. We consider a representative risk-averse investor who has initial endowment in credit instrument and invests in traditional financial instruments. Using the utility indifference pricing approach, we construct a pricing framework for single-name CDS contracts with periodic payments in a multi-period model. Moreover, we analyze CDS buyers with different trading motives, and examine the effect of risk aversion on the indifference upfront prices and credit spreads of CDS for investors with hedging or speculative motives. In particular, our results show that speculation in the CDS market provides a potential explanation for the formation of the negative CDS-bond bases during and after the global financial crisis.

The market impact of systemic risk capital surcharges

AbstractThis paper tests the ‘Too‐Big‐to‐Fail’ hypothesis that whether being designated as a global systemically important bank (G‐SIB) has an impact on the credit default swap (CDS) price of the bank, thereby reducing its credit risk. We find surprising evidence that the CDS spreads of a bank increase (decrease) after the announcement of a higher (lower) capital surcharge. However, this effect is temporary, as the mean CDS spreads revert to preannouncement level, dropping sharply after the initial rise. These findings create a puzzle by implying that a higher capital surcharge requirement and more stringent regulation could outweigh the implicit subsidy advantages of being too‐big‐to‐fail.

CDS Channels of Influence on Discretionary Accruals

Existing studies indicated that firm debt holders can use the credit default swap (CDS) market to hedge their credit risk, and thus they would reduce their monitoring of the firms, leading to largely distressed firms shirking and increasing positive abnormal earnings accruals. Besides providing insurance, however, the CDS spreads also perform price discovery of credit risk information sought by trade creditors and potential lenders who are not protected. High absolute abnormal discretionary accruals or bad earnings quality, especially negative abnormal accruals, would lead adverse CDS price signals that are very costly to the firm. This compels the firm under nondistressed conditions to be able to improve cash holdings, cash flows, working capital, and earnings reporting quality. Our new results indicate that the channels of improvement in earnings quality are through a firm’s large accounts payable and low cash holdings related to trade credit exposures. In the longer run, this leads to higher profitability and improved firm value. Thus, the generation of public information via the CDS market reduces information asymmetry and can enforce greater discipline in discretionary accounts reporting.

Credit default swap prediction based on generative adversarial networks

PurposeFinancial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS).Design/methodology/approachA novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue.FindingsThe authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN.Originality/valueThere are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.

Credit default swap pricing with counterparty risk in a reduced form model with a common jump process

AbstractIn this paper, we study the credit default swap (CDS) pricing with counterparty risk in a reduced form model. The default jump intensities of the reference firm and counterparty are both assumed to follow the mean-reverting CIR processes with independent jumps respectively and a common jump. The approximate closed-form solutions of the joint survival probability density and the probability density of the first default can be obtained by using the PDE method. Then with the expressions of the probability densities, we can get the formula for the CDS price with counterparty risk in a reduced form model with a common jump. In the numerical analysis part, we find that the default of the reference asset has a greater impact on the CDS price than that of the default of counterparty after introducing the common jump process.

COVID-19 and credit risk: A long memory perspective

The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

Sovereign Risk and Government Change: Elections, Ideology and Experience

Global capital markets can react dramatically to elections in developing countries, affecting governments’ access to finance and sometimes setting off broader crises. We argue, contrary to some conventional wisdom, that investors do not systematically react to the election of left-leaning parties and candidates. Government ideology is often an imprecise heuristic, given the diversity in policies among parties, especially those on the left. We therefore expect that neither elections generally, nor elections that produce specific partisan outcomes, are associated with significant changes in sovereign financing costs. Yet we also predict that the election of left-leaning parties will generate volatility in sovereign bond markets, reflecting investors’ uncertainty over future policy outcomes. This volatility is especially pronounced when new governments take office; over time, however, government policy performance enables investors to make increasingly precise estimates of political risk. Volatility has implications for the real economy, as well as for governments’ ability to manage their debt. We test, and find support for, our core expectations using monthly data on sovereign bond spreads and credit default swap prices for 74 developing countries from 1994–2015.

Risk transfer in project finance loans for toll road using credit default swaps

PurposeThe purpose of this paper is to demonstrate the applicability of the Credit Default Swaps (CDS), as a financial instrument, for transferring of risk in project finance loans. Also, an equation has been derived for pricing of CDS spreads.Design/methodology/approachThe debt service cover ratio (DSCR) is modeled as a Brownian Motion (BM) with a power-law model fitted to the mean and half-variance of the existing data set of DSCRs. The survival probability of DSCR is calculated during the operational phase of the project finance deal, using a closed-form analytical method, and the results are verified by Monte Carlo simulation (MCS).FindingsIt is found that using the power-law model yields higher CDS premiums. This in turn confirms the necessity of conducting rigorous statistical analysis in fitting the best performing model as uninformed reliance on constant time-invariant drift and diffusion model can erroneously result in smaller CDS spreads. A sensitivity analysis also shows that the results are very sensitive to the recovery rate and cost of debt values.Originality/valueInsufficiency of free cash flow is a major risk in the toll road project finance and hence there is a need to develop innovative financial instruments for risk management. In this paper, a novel valuation method of CDS is proposed assuming that DSCR follows the BM stochastic process.

A three-factor hazard rate model for single-name credit default swap pricing

We propose a reduced-form model that integrates the modeling of interest rates, equity and default hazard rates. In this model the evolution of the risk-neutral hazard rate is driven by three risk factors: the reference firm’s equity price (in logarithmic form), the risk-free short rate and a latent risk premium factor that is orthogonal to the first two. The equity price and the short rate are considered to be the two primary risk factors, while the risk premium factor is introduced to capture the remaining risks for the hazard rate (such as the liquidity risk). The equity price follows a diffusion process with the possibility of a jump to default, the short rate is governed by a Vasicek model, and the dynamics of the risk premium factor are modeled as a Cox– Ingersoll–Ross process. Under the three-factor model, we derive computable pricing formulas for defaultable zero-coupon bonds, European call options and standard running credit default swaps (CDSs).We then discuss the calibration of the model using CDS and equity option data. Our numerical results reveal that the model is able to interpret a diverse set of CDS spread term structures.

An analytical approximation formula for the pricing of credit default swaps with regime switching

We derive an analytical approximation for the price of a credit default swap (CDS) contract under a regime-switching Black–Scholes model. To achieve this, we first derive a general formula for the CDS price, and establish the relationship between the unknown no-default probability and the price of a down-and-out binary option written on the same reference asset. Then we present a two-step procedure: the first step assumes that all the future information of the Markov chain is known at the current time and presents an approximation for the conditional price under a time-dependent Black–Scholes model, based on which the second step derives the target option pricing formula written in a Fourier cosine series. The efficiency and accuracy of the newly derived formula are demonstrated through numerical experiments. doi:10.1017/S1446181121000274

Analytic Correlation Risk for CDS

We consider a rates credit hybrid model with the rates and the credit intensity governed by Hull-White and Black-Karasinski short-rate models, respectively. We report on and make use of a highly accurate analytic representation of a pricing kernel for European-style options and/or protection payments in powers of the credit spread (not of its lognormal volatility). We show how this can be applied to calculate analytically the impact of rates credit correlation on the pricing of credit default swaps (CDS), extinguishing interest rate swaps, and survival-contingent capped LIBOR flows. Very simple expressions are obtained in all cases for present values (PVs). Highly favorable comparison is found between even the first-order approximations and Monte Carol simulation.

A revised version of the Cathcart & El-Jahel model and its application to CDS market

The paper considers the pricing of credit default swaps (CDSs) using a revised version of the credit risk model proposed in Cathcart and El-Jahel (2003). Default occurs either the first time a signaling process breaches a threshold barrier or unexpectedly at the first jump of a Cox process. The intensity of default depends on the risk-free interest rate, which follows a Vasicek process, instead of a Cox-Ingersoll-Ross process as in the original model. This offers two advantages. On the one hand, it allows us to account for negative interest rates which are recently observed, on the other hand, it simplifies the formula for pricing CDSs. The goodness of fit of the model is tested using a dataset of CDS credit spreads related to European companies. The results obtained show a rather satisfactory agreement between theoretical predictions and market data, which is identical to the one obtained with the original model. In addition, the values of the calibrated parameters result to be stable over time and the semi-closed form solution ensures a very fast implementation.