Difference between revisions of "Proposed projects"
| Line 11: | Line 11: | ||
3. Inflection point indicator - Currency crisis | 3. Inflection point indicator - Currency crisis | ||
| − | The student will learn to | + | The student will learn to predict the currency crisis of a country. The currency crisis predictive model is developed based on machine learning algorithm on historical financial and macroeconomic data. Currency crisis is defined as currency depreciation of at least 25% over a one month period. Relevant data for various countries are FX rate, external debt - short term, External debt – total, Current account deficit, inflation, foreign-direct investment, portfolio or other investment inflows, foreign currency reserves, level of M2/reserves, real interest rate, GDP, equity Index, export and import. We will guide the students both on the modeling and testing to ensure the success of this project. |
For further information on any of the above projects, please contact Chew, Eric <lengsiang.chew@credit-suisse.com>. | For further information on any of the above projects, please contact Chew, Eric <lengsiang.chew@credit-suisse.com>. | ||
Revision as of 19:16, 30 August 2019
Proposals by Credit Suisse
1. Optimal trading strategy
The student will learn the optimal trading strategy that provide the minimum expected cost of trading over a fixed period of time. The theoretical framework is minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. The students are expected to develop the model based on the theoretical framework and to test the model performance by using intraday trading data in the stock market. We will guide students on both modeling and testing to complete this project.
2. Catastrophe stress testing
The student will learn to assess the financial risk of a bank under catastrophe events such as earthquake, tsunami, pandemics and flood. The assessment methodology is based on catastrophe risk model framework developed by the insurance industry. The students will develop the model as well as test the model performance by using historical data such as actual losses, direct and indirect impacts on the economy, corporations, and financial industry due to the catastrophes. We will guide students on both modeling and testing to complete this project.
3. Inflection point indicator - Currency crisis
The student will learn to predict the currency crisis of a country. The currency crisis predictive model is developed based on machine learning algorithm on historical financial and macroeconomic data. Currency crisis is defined as currency depreciation of at least 25% over a one month period. Relevant data for various countries are FX rate, external debt - short term, External debt – total, Current account deficit, inflation, foreign-direct investment, portfolio or other investment inflows, foreign currency reserves, level of M2/reserves, real interest rate, GDP, equity Index, export and import. We will guide the students both on the modeling and testing to ensure the success of this project.
For further information on any of the above projects, please contact Chew, Eric <lengsiang.chew@credit-suisse.com>.
