APPENDING SOUL TO OUR MODEL
On 15.01.2015, Swiss National Bank (SNB) announced it was to impose a negative interest rate of -0.75% on deposit from 22. January. 2015. The sudden announcement shocked the market. Any quantitative interest rate or FX model simply based on recent historical data would fail.
BIG DATA, MACHINE LEARNING AND FINANCE
Big Data has been such a popular term. But what is Big Data exactly? What does it bring us in our daily modeling work? To efficiently analysis and process large quantities of data in the new age, many exceptional technologies have been applied. Machine Learning is a power tool to extract information or pattern within the big data set. There are several Machine Learning approaches ...
Quantitatively construct Portfolio
Designing the correct portfolio cannot be done by human intuition alone. We often hear investors claims “We don’t know at what proportion we should hold those assets, so we invest on them equally.”. Facing the complex financial market consisting of thousands of assets, even the most experienced investors would have difficulties to make decision simply based on intuition. It requires help from sophisticated mathematical tools.
TOO MANY MODELS?
In option valuation, the price is predominantly a function of volatility value. Based on different assumptions, there exists various volatility models. To decide which one to use is not as easy as it sounds. An online dynamic model selection framework should be introduced into our daily work.
MI Bond Pricer
MI Bond Pricer delivers a robust solution for national bond pricing applying several different quantitative interest rate models. The Kalman Filter based optimization method allows MI Bond Pricer to obtain the result more robust and efficient than the traditional ways that simply utilizing Monte Carlo or Least Square Distance method.
MI Volatility Analytics
MI Volatility Analytics is a tool to compute the implied volatility, forward volatility and Greeks of underlying assets. It helps investors to hedge or make decisions based on current market.
MI risk évaluer
MI risk évaluer is a sophisticated quantitative risk management tool that based on Extreme Value Theory (EVT). Different from the traditional methods inherited from Variance - Covariance method or historical method, MI risk évaluer provides a robust, efficient and practical way to calculate (market) risk parameters, (e.g. VaR, Expected Shortfall (ES), Return Level, and etc.), which is required by Basel III framework.
MI CDS Pricer
It provides a Copula based method to price multi-type Credit Default Swap (CDS). It contains several different copula families that differentiate it from the CreditMetrics that lies exclusively on Gaussian Copula. It is shown that CDS price is mainly driven by the copula type, that together with the correlation parameter, they should be calibrated using historical default data and not simply be based on assumptions.
Gaussian Process and supervised Learning
Gaussian Process regression is a non-parametric Bayesian approach. The term non-parametric suggests it lets the data “speak” more for the shape of the model rather than a pre-human-assumed model shape.
WHEN BELOVED normal DISTRIBUTION FAILS
Due to its nice properties, normal distribution is widely used in different fields. E.g. David Li’s model uses Gaussian copula to simulate the joint default distribution to price CDS, which is arguably blamed as “recipe for disaster” of crisis 2008 - 2009. It is known that normal distribution is incapable of modeling heavily tailed problem. What are the alternatives?