RBC-Toronto Quantum Key Distribution Network Development

The interns will develop a quantum communication network built on RBC’s optical fiber infrastructure and perform secure commercial transactions using quantum-generated secure keys in the integrated classical communication network. The quantum communication network will be based on the measurement-device-independent quantum key distribution technology, which is developed by Prof. Hoi-Kwong Lo’s and Prof. Li Qian’s groups at the University of Toronto. It will be an important milestone towards cybersecurity in the financial sector in Canada.

Radio Frequency Identification (RFID) Based Multi Agent System in Banking Environment

The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions.

Sentiment Analysis with Parsed Representation of News Articles

Information published by financial news agencies is used as one of the inputs to make investment decisions. News articles from multiple sources can be used to gauge market sentiment towards an industry or a specific company. Deep learning techniques have been successful in producing state of the art results on various benchmark datasets (Dai & Le, 2015; Miyato et al., 2016). Most of the popular algorithms extract features from words, sentences or paragraphs and represent them as fixed-length vectors (Mikolov et al., 2013; Le & Mikolov, 2014).

Learning representations through stochastic gradient descent by minimizing the cross-validation error

Representations are fundamental to Artificial Intelligence. Typically, the performance of a learning system depends on its data representation. These data representations are usually hand-engineered based on some prior domain knowledge regarding the task. More recently, the trend is to learn these representations through deep neural networks as these can produce significant performance improvements over hand-engineered data representations. Learning representations reduces the human labour involved in any system design, and this allows in scaling of a learning system for difficult problems.

Recurrent Deep Architectures for Modeling Time Series Data

Deep learning is currently the dominant machine learning technique as a result of state of the art performance in vision (Russakovsky, et al., 2015), speech (Amodei, et al., 2015) and natural language processing (Vinyals et al., 2015). The improvement in performance of these models is attributed to the availability of large datasets for training the models as well as software & hardware improvements that help accelerate the training process. Recurrent Neural Networks (RNNs) are one of the most powerful and popular frameworks for modeling sequential data such as speech and text.

Prediction and optimal strategies in equity algorithmic trading

Trading is increasingly moving from the human world to its electronic counterpart. In this new environment the effects need to be properly understood and analyzed. Most recently the Canadian market as other developed capital markets has experienced a reduction in direct trading costs for investors but at the cost of an increase in indirect trading costs, price variability.

Risk minimizing hedging strategy of variable annuity guarantees under stochastic interest rates

A hedge is an investment position intended to offset potential losses, or in our case to pay off potential liabilities. Interest rates play an important role in hedging strategies and risk management for variable annuities and other long-term products. Financial institutions have an urgent need for practical and affordable dynamic hedging strategies. We propose a realistic interest rates model and the so-called risk minimization hedging strategy.

Effect of Foreign Exchange Rates on the Default Correlation

Default correlation analysis has an important role in asset pricing and credit risk management. Our proposed default model aims to analyze the default correlation for two international companies. In this analysis, we would like to incorporate existing correlation between the stock indices in different countries and study its effect on the default correlation measure. Moreover, there is evidence in the literature of sensitivity of equity index returns to foreign exchange (FX) rates.

Investigation of Methodologies for Counterparty Credit Risk and Credit Value Adjustment Calculation

The research project is to investigate a multi]factor multi]asset extension of the Hestonf93 stochastic volatility model for comprehensive Credit Value Adjustment calculation and Commodity Counterparty Credit Risk methodology. The extended model covers counterparty hazard rates correlated with the underlying and interest rates in order to model wrong]way exposure.

Modelling and Computation of Credit Risk and Credit Derivatives

For the past 10 years, the credit derivative markets have experienced unprecedented growth. Combined with this expansion, credit risk modelling has become quite a challenging task for many financial institutions. The Royal Bank of Canada has substantial trading exposure in credit derivatives and is looking to further its modelling research and management process.