Jul
7
Examining the determinants of credit default swap (CDS) spreads is a hot topic, CDS spread has displayed siginificant regime switching behaviour since the break of credit crisis, which can be seen from the old graph in the post Credit Default Spread and Historical Volatility
There are sound reasons to believe that CDS spreads keep high in the period of turbulence while stay stably low during most of quiet periods. To investigate if there is possible regime switch phenomenon, I run a three year rolling panel regression using CDSs of over 250 reference entities on several widely accepted explanatory variables including: leverage, volatility, treasury yield and the spread of three month Libor and repo rates, where the last variable is used to proxy liquidity risk. The coefficients for each variable is plotted below
the coefficients of leverage and treasury yields are changing but without clear regime pattern, on the contrary, the volatility, especially the liquidity effects are suggesting there may exist regime switching and the necessity to employ a Markov regime switch model to explain CDS spreads.
PS: a matlab markov regime switching package can be found here; the panel regression is done with the R package PLM at http://cran.r-project.org/web/packages/plm/vignettes/plm.pdf
There are sound reasons to believe that CDS spreads keep high in the period of turbulence while stay stably low during most of quiet periods. To investigate if there is possible regime switch phenomenon, I run a three year rolling panel regression using CDSs of over 250 reference entities on several widely accepted explanatory variables including: leverage, volatility, treasury yield and the spread of three month Libor and repo rates, where the last variable is used to proxy liquidity risk. The coefficients for each variable is plotted below
the coefficients of leverage and treasury yields are changing but without clear regime pattern, on the contrary, the volatility, especially the liquidity effects are suggesting there may exist regime switching and the necessity to employ a Markov regime switch model to explain CDS spreads.
PS: a matlab markov regime switching package can be found here; the panel regression is done with the R package PLM at http://cran.r-project.org/web/packages/plm/vignettes/plm.pdf
Jul
1
To be honest, I haven't read this paper yet as my research interest has moved gradually from no-arbitrage to arbitrage valuation, however, this paper Adding and Subtracting Black-Scholes:A New Approach to Approximating Derivative Prices in Continuous-Time Models is very interesting from its abstract and may be appealing to some of you.
Adding and Subtracting Black-Scholes: A New Approach to Approximating Derivative Prices in Continuous-Time Models is written by Dennis Kristensen, Antonio Mele, and is accepted by Journal of Financial Economics.
A working paper is available at http://w4.stern.nyu.edu/volatility/docs/Kristensen.pdf
Adding and Subtracting Black-Scholes: A New Approach to Approximating Derivative Prices in Continuous-Time Models is written by Dennis Kristensen, Antonio Mele, and is accepted by Journal of Financial Economics.
Quotation
We develop a new approach to approximating asset prices in the context of continuous-time models. For any pricing model that lacks a closed-form solution, we provide a solution, which relies on the approximation of the intractable model through a known, "auxiliary" one. We derive an expression for the difference between the true (but unknown) price and the auxiliary one, which we approximate in closed-form, and use to create increasingly improved refinements to the initial mispricing induced by the auxiliary model. The approach is intuitive, simple to implement and leads to fast and extremely accurate approximations. We illustrate this method in a variety of contexts, including option pricing with stochastic volatility, volatility contracts and the term-structure of interest rates.
A working paper is available at http://w4.stern.nyu.edu/volatility/docs/Kristensen.pdf
Jun
30
Tactical asset allocation (TAA) is a dynamic investment strategy that actively adjusts a portfolio’s asset allocation in order to improve the risk-adjusted returns of passive management investing. We know the performance of debt assets and equity are correlated somehow, this debt-equity relationship can be exploited profitably at the level of both individual companies and the market as a whole, for instance, if a company’s credit is going to outperform its equity, then a trade can be constructed to buy debt and sell (short) stock.
In the paper Credit Informed Tactical Asset Allocation by David Klein, he outlines a tactical asset allocation strategy that takes signals from the credit markets and applies them to the stock market. The strategy rules are straightforward:
1. If stocks appear undervalued relative to corporate bonds, go long stocks.
2. If stocks appear overvalued relative to corporate bonds, exit stock positions and buy short-term Treasuries.
the back-test of the strategy captures 65% of upside equity moves on a monthly basis while only taking 21% of the downside.
A comparison of this strategy with buy-and-hold is summarized
For detail please refer to the paper Credit Informed Tactical Asset Allocation downloadable at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1872163.
In the paper Credit Informed Tactical Asset Allocation by David Klein, he outlines a tactical asset allocation strategy that takes signals from the credit markets and applies them to the stock market. The strategy rules are straightforward:
1. If stocks appear undervalued relative to corporate bonds, go long stocks.
2. If stocks appear overvalued relative to corporate bonds, exit stock positions and buy short-term Treasuries.
the back-test of the strategy captures 65% of upside equity moves on a monthly basis while only taking 21% of the downside.
A comparison of this strategy with buy-and-hold is summarized
For detail please refer to the paper Credit Informed Tactical Asset Allocation downloadable at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1872163.
Jun
22
Credit Default Spread (CDS) reflects the default risk of a company, Zhong, Cao et al. (2010) argue CDS is similar to out-of-the-money put options in that both offer a low cost and effective protection against downside risk. They then investigates that put option-implied volatility is an important determinant of CDS spreads.
Since I can't get access to OptionMetrics database, I plot a graph showing the relation between average daily 5-year CDS downloaded from CMA, Datastream and simple average historical volatility measured by exponentially weighted moving average (EWMA) of 355 US entities, how amazingly close is the co-movement of these two series.
Reference:
Zhong, Z. D., C. Cao, et al. (2010). "The information content of option-implied volatility for credit default swap valuation." Journal of Financial Markets 13(3): 321-343.
Since I can't get access to OptionMetrics database, I plot a graph showing the relation between average daily 5-year CDS downloaded from CMA, Datastream and simple average historical volatility measured by exponentially weighted moving average (EWMA) of 355 US entities, how amazingly close is the co-movement of these two series.
Reference:
Zhong, Z. D., C. Cao, et al. (2010). "The information content of option-implied volatility for credit default swap valuation." Journal of Financial Markets 13(3): 321-343.
Jun
21
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques.
Contents include:
Financial markets, prices and risk
Univariate volatility modeling
Multivariate volatility models
Risk measures
Implementing risk forecasts
Analytical value-at-risk for options and bonds
Simulation methods for VaR for options and bonds
Backtesting and stress testing
Extreme value theory
Endogenous risk
You can download the Matlab and R codes at http://www.financialriskforecasting.com/book-code, I would recommend the book “Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab” to anyone who work as a risk analyst and need an introductory, practical book, on top of that, with enough programming codes to play with.
Contents include:Financial markets, prices and risk
Univariate volatility modeling
Multivariate volatility models
Risk measures
Implementing risk forecasts
Analytical value-at-risk for options and bonds
Simulation methods for VaR for options and bonds
Backtesting and stress testing
Extreme value theory
Endogenous risk
You can download the Matlab and R codes at http://www.financialriskforecasting.com/book-code, I would recommend the book “Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab
” to anyone who work as a risk analyst and need an introductory, practical book, on top of that, with enough programming codes to play with.
