New general dependence measures: construction, estimation and application to high-frequency stock returns

We propose a set of dependence measures that are non-linear, local, invariant to a wide range of transformations on the marginals, can show tail and risk asymmetries, are always well-defined, are easy to estimate and can be used on any dataset. We propose a nonparametric estimator and prove its consistency and asymptotic normality. Thereby we significantly improve on existing (extreme) dependence measures used in asset pricing and statistics. To show practical utility, we use these measures on high-frequency stock return data around market distress events such as the 2010 Flash Crash and during the GFC. Contrary to ubiquitously used correlations we find that our measures clearly show tail asymmetry, non-linearity, lack of diversification and endogenous buildup of risks present during these distress events. Additionally, our measures anticipate large (joint) losses during the Flash Crash while also anticipating the bounce back and flagging the subsequent market fragility. Our findings have implications for risk management, portfolio construction and hedging at any frequency

Decentralized Matrix Factorization with Heterogeneous Differential Privacy

Conventional matrix factorization relies on centralized collection of users' data for recommendation, which might introduce an increased risk of privacy leakage especially when the recommender is untrusted. Existing differentially private matrix factorization methods either assume the recommender is trusted, or can only provide a uniform level of privacy protection for all users and items with untrusted recommender. In this paper, we propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as HDPMF) for untrusted recommender. To the best of our knowledge, we are the first to achieve heterogeneous differential privacy for decentralized matrix factorization in untrusted recommender scenario. Specifically, our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade off between privacy and accuracy. Meanwhile, by allocating privacy budget properly, we can capture homogeneous privacy preference within a user/item but heterogeneous privacy preference across different users/items. Theoretical analysis confirms that HDPMF renders rigorous privacy guarantee, and exhaustive experiments demonstrate its superiority especially in strong privacy guarantee, high dimension model and sparse dataset scenario.Comment: Accepted by the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023

The impact of regulation, governance, market power and diversification on bank performance and risk

This thesis examines the impact of banking regulation, external governance and bank-specific variables on commercial and savings bank performance, as estimated by efficiency and financial indicators, in the Asian market, between 2000 and 2012. Furthermore, the thesis analyses the effect of deposit diversification and insurance on the bank’s liquidity risk tolerance in G7 and BRICS countries. It further investigates the impact of expected government support on bank risk-taking in China. Firstly, we examine the impact of Credit Rating Agencies (CRAs) on bank performance in general, and in particular on how this impact can be moderated by the strict regulation of banking criteria and the quality of investor protection embedded in different institutional environments. We find that CRAs enhances bank performance. CRAs as the flexible governance power, their positive monitoring impact is further enhanced by the quality of investor protection but mitigated by the inflexible and strict banking regulations. Secondly, this research investigates the impact of market power and revenue diversification on bank performance and stability. We find that market power could not only improve banking performance, but also increase individual bank fragility in an emerging market. Although revenue diversification reduces bank efficiency, it improves individual stability. Thirdly, we study the relationship between liquidity risk, deposit diversification and insurance in 12 countries during the period 2005-2014. We capture liquidity risk by focusing on the unfunded loan commitments. We find that higher diversification in the deposit base can reduce the impact of liquidity demand risk during the crisis by decreasing the cost of funding, increasing the funding inflow, maintaining the total amount of loan lending and enhancing the liquid ratio. Additionally, the results suggest that although deposit insurance has a positive impact during the crisis, its effect cannot mitigate the liquidity demand risk. Fourthly, this research examines the impacts of expected government support on bank risk-taking behaviour, and in particular how its impact can be stronger in state-owned and large banks. We find that the willingness and capacity of government support enhance bank’s risk-taking behaviour through increasing non-performance loan as well as doubtful loan, and decreasing Z-score as well as liquid ratio. This moral hazard problems are further enhanced in state-owned banks and large banks. Finally, we outline our conclusions along with the limitations of this research and a plan for any future work

MAPPING THE RIGID AND FLEXIBLE REGIONS OF A NOVEL SECRETED PROTEIN EVPP FROM THE T6SS OF EDWARDSIELLA TARDA

Ph.DDOCTOR OF PHILOSOPH