By Max C. Y. Wong
Introduces a strong new method of monetary danger modeling with confirmed thoughts for its real-world applications
The 2008 credits obstacle did a lot to debunk the a lot touted powers of worth in danger (VaR) as a hazard metric. not like so much authors on VaR who specialize in what it may do, during this publication the writer appears to be like at what it can't. In transparent, available prose, finance practitioners, Max Wong, describes the VaR degree and what it used to be intended to do, then explores its numerous disasters within the actual global of main issue chance administration. extra importantly, he lays out a progressive new approach to measuring dangers, Bubble worth in danger, that's countercyclical and provides a well-tested buffer opposed to industry crashes.
- Describes Bubble VaR, a extra macro-prudential danger degree confirmed to prevent the restrictions of VaR and through delivering a extra actual possibility publicity estimation over industry cycles
- Makes a powerful case that analysts and chance managers have to unlearn our current "science" of probability dimension and detect extra powerful methods to calculating chance capital
- Illustrates each key notion or formulation with an abundance of functional, numerical examples, such a lot of them supplied in interactive Excel spreadsheets
- Features various real-world functions, all through, in accordance with the author’s firsthand event as a veteran monetary danger analyst
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Extra resources for Bubble Value at Risk: A Countercyclical Risk Management Approach
It is instructive to compare four well-known models of volatility—standard deviation, EWMA, ARCH, and GARCH. Assuming we have a rolling window of n days with price data p1, . . , pn, if volatility is constant or varying slowly, we can estimate the nth day volatility by: St: Dev: model : σ ^ 2n ¼ n 1X r2 n i¼1 i ð2:26Þ where ri is the observed percentage return (pi À piÀ1)/piÀ1 or log return ln (pi/piÀ1). 3) but with zero mean. Therefore, volatility σ ^ (normally written without the subscript n) is simply the standard deviation of returns.
Data does not make TA any less effective. It does, however, mean that the method is less consistent in a statistical sense. Thus, TA has always been regarded as unscientific by academia. ). Market momentum is a known fact. d. does not make risk measurement more scientific. There is no compelling reason why risk management cannot borrow some of the modelling techniques of TA such as that pertaining to momentum and cycles. From an epistemology perspective, such debate can be seen as a tacit choice between intuitive knowledge (heuristics) and mathematical correctness.
Random variable εt. 2. d. process will be stationary. d. process (like the AR(1)) is necessarily nonstationary. Indeed, it is stationary given certain conditions (|k1| < 1). 3. It is always serially correlated as shown by its ACF. , stationarity and serial correlation are related yet distinct ideas. Only under certain conditions, does one lead to another. 5 REGRESSION MODELS AND RESIDUAL ERRORS Regression is a basic tool in time series modeling to find and to quantify relationships between variables.