Our Stata | Mata code offers a comprehensive implementation of the Merton distance to default or Merton DD model, following the iterative process employed by renowned researchers such as Crosbie and Bohn (2003), Vassalou and Xing (2004), and Bharath and Shumway (2008). By utilizing our code, you can easily apply the model using the following steps:

1.Generate log returns from stock prices.

2. Calculate the volatility for each stock in each year using daily stock returns, subsequently converting it into yearly volatility.

3. Utilize the stock volatility as a proxy for asset volatility in the initial iteration, employing a specific equation to infer the firm value.

E = V\mathcal{N}(d_1) - e^{-rT}F\mathcal{N}(d_2),

Here, E represents the market value of the firm’s equity, F denotes the face value of the firm’s debt, r is the risk-free rate, and \mathcal{N}(.) represents the cumulative standard normal distribution function. Additionally, d_1 and d_2 are defined as follows:

d_1 = \dfrac{ln(V/F) + (r+0.5\sigma_V^2)T}{\sigma{_V}\sqrt{T}}
d_2 = d_1-\sigma{_V}\sqrt{T}

4. Repeat step 3 for each observation while employing the volatility values of a particular firm in a given year. This iterative process generates daily firm values. It’s important to note that the root for the firm value in Equation 1 needs to be found, and we have set the iteration limit at 1000.

5. Once the implied firm values are generated, recalculate log returns based on these values.

6. Re-evaluate daily volatility from the log returns for each firm-year combination, and convert it back into yearly volatility.

7. Plug the new volatility values into Equation 1 to infer firm values.

8. Iterate this process (up to 500 times) until the new volatility value converges to the previous volatility.

9. After achieving convergence, employ the firm and volatility values from the last successful iteration to derive the probability of default using the following equation:

\mathcal{N}\left(-\left(\dfrac{ln(V/F)+(\mu-0.5\sigma_V^2)T}{\sigma{_V}\sqrt{T}}\right)\right)= \mathcal{N}(-DD)

 

Accuracy of the Code

We have rigorously tested the code with both real and dummy data, and it consistently produces the expected results, accurately matching the benchmarks created during the construction of the dummy data. 
 

  Pricing

The code is available for purchase at £139. We also offer a data processing service for £49 to help you prepare your data for use with the code. We can modify your data set and convert it to Stata format, or we can construct the required variables if needed. For further details and inquiries, please don’t hesitate to contact us at the following:

attaullah.shah@imsciences.edu.pk

Stata.Professor@gmail.com

         

Why should you buy the code?

 

Created by an Expert

The code is written by Dr. Prof. Attaullah Shah, an experienced finance professional with over 20 years of experience in coding and quantitative modeling. He has published many Stata programs demonstrating his expertise.

Customizable and Extensible

While implementing the core model, the code is structured and commented in a way that makes it easy to customize and extend for your own purposes. Adapt it to your specific needs.

Save Time and Effort Developing Your Own Code

The code on the page implements the complex KMV Merton model for calculating distance to default and default probabilities. Developing this from scratch would take considerable time and effort.

Accurate and Well-Tested Implementation

The code has been thoroughly tested and validated to ensure it accurately implements the published KMV Merton model. You can trust it to produce correct results.

Efficient and Optimized

The code has been optimized to run efficiently on large datasets. It leverages vectorization and other techniques to maximize performance.

Expert Support

The code comes with expert support to help you implement and customize it for your needs. Get answers and guidance from the creator.

Affordable Pricing

For a very reasonable price, you get reliable, optimized code plus support. Far more affordable than building it yourself.

Pay with PayPal

Paypal offers a secure and convenient payment solution. To pay with PayPal,

 

After receiving your payment, we shall send the code and example dataset within 24 hours. Please email your Paypal transaction details at attaullah.shah@imsciences.edu.pk

  References

Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. The Review of Financial Studies, 21(3), 1339-1369.

Crosbie, P., & Bohn, J. (2003). Modeling Default Risk: Modeling Methodology, Moody’s KMV Company. available at http:\\www.moodyskmv.com.

Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.

Vassalou, M., & Xing, Y. (2004). Default risk in equity returns. The journal of finance, 59(2), 831-868.

 

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