Hedging stock sector risk with credit default swaps

Ratner, Mitchell; Chiu, Chih-Chieh

doi:10.1016/j.irfa.2013.05.001

Cited by 110 publications

(71 citation statements)

References 38 publications

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“…In addition, CDS sector indices are based on the most liquid 5-year term, are equally weighted, and reflect an average midspread calculation of the given index's constituents. 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].…”

Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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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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Section: Introductionmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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“…Following Ratner and Chiu (), we use three models to test the US$ as a hedge and safe haven against stock market risk. The first and the second models examine the hedge and safe haven characteristics of US$ during periods of extreme negative stock returns and extreme stock volatility.…”

Section: Resultsmentioning

confidence: 99%

“…We assume that the value of the US$ against Greater China currencies depends on changes in the Greater China stock markets. Hence, to examine the hedge and safe haven characteristics of the US$ against stock markets during periods of extreme negative stock returns, we adapt Ratner and Chiu's () dummy variable regression model as follows:

{normalρ}_{t}^{DCC} = {normalγ}_{0} + {normalγ}_{1} D false(r_{s t o c k q 10} false) + {normalγ}_{2} D false(r_{s t o c k q 5} false) + {normalγ}_{3} D false(r_{s t o c k q 1} false),

where

ρ_{t}^{italicDCC}

are the time‐varying correlations obtained from Equation , which we regress on three dummy variables representing market turmoil. D represents the dummy variables that capture extreme stock market movements equal to one if the stock market exceeds the 10%, 5% and 1% quantile of the negative return distribution.…”

Section: Methodsmentioning

confidence: 99%

Structural breaks, dynamic correlations, and hedge and safe havens for stock and foreign exchange markets in Greater China

Dong

Yoon

2017

World Economy

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| INTRODUCTIONDuring the past few decades, the relationship between stock prices and exchange rates has attracted special attention from economists, portfolio risk managers and policymakers. These two variables are important and intrinsically linked macrofinancial variables. Previous literature provides two theoretical aspects to explain the interactions between stock prices and exchange rates.On the one hand, Dornbusch and Fischer (1980) develop the "flow-oriented" models of exchange rates that focus on the current account or the trade balance, and posit that the changes in exchange rates will affect international competitiveness and trade balances directly, thereby influencing countries' real incomes and output. Since stock prices are generally interpreted as the present values of firms' future cash flows, they respond to exchange rate changes. This is consistent with the international trading effect (Aggarwal, 1981); thus, changes in exchange rates affect stock prices positively via international competitiveness and trade balances. However, we argue that this model does not account for all cases influenced by exchange rate changes. For example, sometimes, domestic currency depreciation increases the pressure of domestic capital outflow, and domestic investors will change domestic currency into foreign currency or foreign currency-denominated financial products, thereby reducing the domestic money supply and stock prices. In the latter situation, changes in exchange rates affect stock prices negatively via the domestic capital outflow.On the other hand, Branson (1983) and Frankel (1983) present the "stock-oriented" models of exchange rates that focus on the impact of stock prices on exchange rates, which state that changes in stock prices will affect domestic capital inflows and outflows, thereby affecting the demand for money, which then leads to changes in interest rates and exchange rate movements. This is in agreement with the portfolio balance effect (Bahmani-Oskooee & Sohrabian, 1992) and monetarist models of exchange rate determination (Gavin, 1989); thus, changes in stock prices affect exchange rates negatively via capital mobility. The negative relationship between these two markets would have important implications for investors in terms of diversifying benefits.Since the Chinese government opened the Shanghai and Shenzhen stock exchange markets in 1990 and 1991, respectively, the Greater China stock markets (namely, Shanghai, Shenzhen, Hong Kong and Taiwan) have grown quickly and become increasingly attractive to foreign capital. Moreover, after the 2008-09 global financial crisis (GFC), these emerging economies applied rapid financial reform to move towards globalisation (Lin & Fu, 2016), thus increasing their interaction with the global economy through capital flows. Richards (2005) indicates that foreign capital flows can increase and decrease stock prices, and capital inflows and stock returns are positively related, particularly in emerging markets. Thus, considerable quantities of international...

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“…In order to thoroughly assess the hedging and safe haven characteristics of token returns we follow the procedure used by Baur and McDermott (2010) to assess the features of gold as an asset class 6 . Nevertheless, our algorithm resembles more closely that of Ratner and Chiu (2013) who examined the hedging properties of CDS versus the stock market using DCC as estimation procedure for the comovements of assets.…”

Section: Hedge and Safe Haven Detection Modelmentioning

confidence: 99%

Crypto assets: the role of ICO tokens within a well-diversified portfolio

Adhami

Guégan

2019

J. Ind. Bus. Econ.

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This paper reexamines the discussion on blockchain technology, crypto assets and ICOs, providing also evidence that in crypto markets there are currently two classes of assets, namely standalone cryptocurrencies (or 'coins') and tokens, which result from an ICO and are intrinsically linked to the performance of the issuing company or venture. While the former have been arguments of various empirical studies regarding their price dynamics and their effect on the variance of a well-diversified portfolio, no such study has been done to analyze listed tokens, which in our sample are over 700 and with a backing of about $17.3Bn from their respective ICOs. Therefore, investors interested in optimizing their portfolios should first assess the diversifier, hedge or safe haven role of tokens vis-àvis traditional assets, on top of 'coins', in order to sensibly use this new asset class. After constructing various indices to represent both the token asset class as a whole and its sub-classes, we model dynamic conditional correlations among all the assets in our sample to obtain time-varying correlations for each token-asset pair. We find that tokens are effective diversifiers but not a hedge or a safe haven asset. We evidence that tokens retain important systematic differences with the two other asset classes to which they are most generally compared to, namely 'coins' and equities.

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Hedging stock sector risk with credit default swaps

Cited by 110 publications

References 38 publications

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Structural breaks, dynamic correlations, and hedge and safe havens for stock and foreign exchange markets in Greater China

Crypto assets: the role of ICO tokens within a well-diversified portfolio

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