Abstract:This paper examines risk transmission and migration among six US measures of credit and market risk during the full period 2004-2011 period and the 2009-2011 recovery subperiod, with a focus on four sectors related to the highly volatile oil price. There are more long-run equilibrium risk relationships and short-run causal relationships among the four oil-related Credit Default Swaps (CDS) indexes, the (expected equity volatility) VIX index and the (swaption expected volatility) SMOVE index for the full period… Show more
“…In addition, a dynamic conditional correlation matrix may be obtained only through the standardization in Equation ( 7). However, we can also note an inconsistency between the dynamic conditional expectation reported in Equation ( 5) and the way in which the dynamic conditional correlation matrix is obtained in Equation (7). Such inconsistency causes further problems as t Q is not the conditional covariance of t η , as shown in Equation ( 5), and is not the conditional correlation of t η as it is just positive definite, but need not correspond to a dynamic conditional correlation matrix.…”
Section: Is Based On the Conditional Second-order Moment Of The Standardized Residuals And Hence Does Not Directly Yield Conditional Corrmentioning
confidence: 96%
“…In particular, there has been great emphasis paid to the analysis of financial assets (see [1] and [2], among others, and the references cited in the surveys by [3] and [4]). More recently, there has been growing interest in energy finance, particularly oil (see [5], [6], [7] and [8] among others).…”
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for
caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic
conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators;
DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news
is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model
“…In addition, a dynamic conditional correlation matrix may be obtained only through the standardization in Equation ( 7). However, we can also note an inconsistency between the dynamic conditional expectation reported in Equation ( 5) and the way in which the dynamic conditional correlation matrix is obtained in Equation (7). Such inconsistency causes further problems as t Q is not the conditional covariance of t η , as shown in Equation ( 5), and is not the conditional correlation of t η as it is just positive definite, but need not correspond to a dynamic conditional correlation matrix.…”
Section: Is Based On the Conditional Second-order Moment Of The Standardized Residuals And Hence Does Not Directly Yield Conditional Corrmentioning
confidence: 96%
“…In particular, there has been great emphasis paid to the analysis of financial assets (see [1] and [2], among others, and the references cited in the surveys by [3] and [4]). More recently, there has been growing interest in energy finance, particularly oil (see [5], [6], [7] and [8] among others).…”
The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for
caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does not yield dynamic
conditional correlations; DCC is stated rather than derived; DCC has no moments; DCC does not have testable regularity conditions; DCC yields inconsistent two step estimators;
DCC has no asymptotic properties; DCC is not a special case of Generalized Autoregressive Conditional Correlation (GARCC), which has testable regularity conditions and standard asymptotic properties; DCC is not dynamic empirically as the effect of news
is typically extremely small; DCC cannot be distinguished empirically from diagonal Baba, Engle, Kraft and Kroner (BEKK) in small systems; and DCC may be a useful filter or a diagnostic check, but it is not a model
“…Oil prices, the volatility index (VIX), and gold prices are examples of international factors affecting CDS spreads. Many scholars determine that CDS spreads are influenced by oil prices (Duffie et al, 2003;Arouri et al, 2011;Hammoudeh et al, 2013;Lahiani et al, 2016;Pavlova et al, 2018;Yang et al, 2018;Bouri et al, 2020;Wang et al, 2020). Besides, VIX explains changes in CDS spreads which implies the default risk of countries (Che & Kapadia, 2012).…”
The study aims to define the sources of Turkey's sovereign CDS spread changes to develop policies that stabilize CDS spreads since they have a volatile and increasing trend, especially in the last two years.In this context, monthly data of 13 factors related to international, macroeconomic, and market between 2011/1 and 2019/12 are used by dividing the dataset into three periods as the full period (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), the stability period (2011)(2012)(2013)(2014)(2015)(2016)(2017), and the macroeconomic turbulent period (2018)(2019) and performing 4 different machine learning algorithms. The empirical results prove that (i) Treasury bond interest rate should be lower than 8% in the stability period and gold prices should be lower than TL 5.500 in the macroeconomic turbulent period to have low-level CDS spreads; (ii) NPL volume has no significant effect on in any period examined; (iii) the significance of factors on sovereign CDS spreads vary over the periods.
“…However, single-name CDS spreads are much less liquid than indices [17][18][19]. In several studies, the creditworthiness of individual industries was investigated using CDS sector data [19][20][21][22]. e CDS term structure is important because it integrates the future risk expectations of both markets and companies by offering CDS spreads over time.…”
In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.
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