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How to handle negative short term rates

How to handle negative short term rates

There is a research paper on this exact topic

Abstract:

We discuss a simple extension of the Ho and Lee model with generic time-dependent drift in which: 1) we compute bond prices analytically; 2) the yield curve is sensible and the asymptotic yield is positive; and 3) our analytical solution provides a clean and simple way of separating volatility from the drift in the short-rate process. Our extension amounts to introducing one or two reflecting barriers for the underlying Brownian motion (as opposed to the short-rate), which allows to have more realistic time-dependent drift (as opposed to constant drift). In our model the spectrum — or, roughly, the set of short-rate values contributing to bond and other claim prices — is discrete and positive. We discuss how to calibrate our model using empirical yield data by fitting three parameters and then read off the time-dependent drift.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2562500

 

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Meet my new open source Quan trader trading platform #open-source #trading #Quant #H FT

Meet my new open source Quan trader trading platform #open-source FT

[igp-video src=”https://quantlabs.net/blog/wp-content/uploads/2015/03/Meet-my-new-open-source-Quan-trader-trading-platform-open-source-trading-Quant-H-FT.mp4″ poster=”https://quantlabs.net/blog/wp-content/uploads/2015/03/Meet-my-new-open-source-Quan-trader-trading-platform-open-source-trading-Quant-H-FT.jpg” size=”large”]
Meet my new open source Quan trader trading platform #open-source #trading #Quant #H FT

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Is the CFA helpful for a career in trading or mainly just for one in corporate finance? I appreciate your opinions and experience

Is the CFA helpful for a career in trading or mainly just for one in corporate finance? I appreciate your opinions and experience

 

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I’ll take a stab since no one else is: while I don’t have a CFA, many people in the industry have taken one, partly to help with a post-trading career, as well as to gain broader insight into the financial marketplace and the products they are interested in trading.

The main thing you get from the CFA which is directly applicable is a rigorous understanding of the financial aspects of the various instruments and their inter-relationships. One cannot trade these markets intelligently without that understanding. You will learn about virtually every financial product under the sun, and while you won’t ever trade 95% of them, it is valuable to have that appreciation for their place in the scheme of things nonetheless. Basically, you’ll learn more than you need to know, but that’s always better than the opposite condition.

So it may not be the ideal way to prepare for trading, but it will certainly help in many ways, and will additionally have other benefits outside of trading. I hear that Level 1 is tough and Level 2 is even more difficult. Pass that and Level 3 should be a relative breeze. Best of luck.

 

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sHave you looked at the Certificate in Quantitative Finance (CQF)?
I ask as i see a lot of discussions what is best to get MBA, CFA, or CQF

There is a LinkedIn group you may want to ask in there, they should be able to provide more useful information

http://www.linkedin.com/groups/Certificate-in-Quantitative-Finance-CQF-738597

Here for more details

www.cqf.com
or
http://www.wilmott.com/cqf.cfm

 

 

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MATLAB Econometrics Toolkit: My GARCH highlight picks for forecasting time series model with time series

MATLAB Econometrics Toolkit: My GARCH highlight picks for forecasting time series model with time series

In Matlab Econometrics Toolkit help”

In mathematics, univariate refers to an expression, equation, function or polynomial of only one variable.
In Matlab Econometrics Toolkit
Time Series Modelling
Forecasting Time Series
This random sequence, or stochastic process, may exhibit a degree of correlation from one observation to the next. You can use this correlation structure to predict future values of the process based on the past history of observations.
{?t} is a random innovations process. It represents disturbances in the mean of {yt}. You can also interpret ?t as the single-period-ahead forecast error.

GARCH models are consistent with various forms of efficient market theory, which states that observed past returns cannot improve the forecasts of future returns. Correspondingly, GARCH innovations {?t} are serially uncorrelated.
The general ARMAX(R,M,Nx) model for the conditional mean

applies to all variance models with autoregressive coefficients {?i}, moving average coefficients {?j}, innovations {?t}, and returns {yt}. X is an explanatory regression matrix in which each column is a time series. X(t,k) denotes the tth row and kth column of this matrix.

The eigenvalues {?i} associated with the characteristic AR polynomial

must lie inside the unit circle to ensure stationarity
GARCH Models
GARCH stands for generalized autoregressive conditional heteroscedasticity. The word “autoregressive” indicates a feedback mechanism that incorporates past observations into the present. The word “conditional” indicates that variance has a dependence on the immediate past. The word “heteroscedasticity” indicates a time-varying variance (volatility).
GARCH, then, is a mechanism that includes past variances in the explanation of future variances. More specifically, GARCH is a time series technique used to model the serial dependence of volatility.
You can apply GARCH models to such diverse fields as:

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Risk management
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Portfolio management and asset allocation
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Option pricing
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Foreign exchange
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The term structure of interest rates

You can find significant GARCH effects in equity markets [7] for:

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Individual stocks
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Stock portfolios and indices
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Equity futures markets

GARCH effects are important in areas such as value-at-risk (VaR) and other risk management applications that concern the efficient allocation of capital.

You can use GARCH models to:

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Examine the relationship between long- and short-term interest rates.
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Analyze time-varying risk premiums [7] as the uncertainty for rates over various horizons changes over time.
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Model foreign-exchange markets, which couple highly persistent periods of volatility and tranquility with significant fat-tail behavior [7].

EGARCH models are fundamentally different from GARCH and GJR models in that the standardized innovation, zt, serves as the forcing variable for both the conditional variance and the error. GARCH and GJR models allow for volatility clustering via a combination of the Gi and Aj terms. The Gi terms capture volatility clustering in EGARCH models.
GARCH(1,1) model, which is by far the most common model.
Although EGARCH models require no parameter constraints to ensure positive conditional variances, stationarity constraints are necessary. The Econometrics Toolbox software treats EGARCH(P,Q) models as ARMA(P,Q) models for the logarithm of the conditional variance. Therefore, this toolbox imposes nonlinear constraints on the Gi coefficients to ensure that the eigenvalues of the characteristic polynomial are all inside the unit circle.
For EGARCH models, the leverage coefficients Li apply to the actual innovations ?t-1. For GJR models, the leverage coefficients enter the model through a Boolean indicator, or dummy, variable. Therefore, if the leverage effect does indeed hold, the leverage coefficients Li should be negative for EGARCH models and positive for GJR models. This is in contrast to GARCH models, which ignore the sign of the innovation.

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