Research articles for the 2019-08-20

A Review of Changepoint Detection Models
Yixiao Li,Gloria Lin,Thomas Lau,Ruochen Zeng

The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of the changepoint detection. The next stage is to elaborate traditional and some alternative model-based changepoint detection algorithms. Finally, we try to go a bit further in the theory and look into future research directions.

A lognormal type stochastic volatility model with quadratic drift
Peter Carr,Sander Willems

This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant level, with a speed of mean reversion that is affine in the instantaneous volatility level. The steady-state distribution of the instantaneous volatility belongs to the class of Generalized Inverse Gaussian distributions. We show that the quadratic term in the drift is crucial to avoid moment explosions and to preserve the martingale property of the stock price process. Using a conveniently chosen change of measure, we relate the model to the class of polynomial diffusions. This remarkable relation allows us to develop a highly accurate option price approximation technique based on orthogonal polynomial expansions.

Aggregate Confusion: The Divergence of ESG Ratings
Berg, Florian,Kölbel, Julian F,Rigobon, Roberto
This paper investigates the divergence of environmental, social, and governance (ESG) ratings. First, the paper documents the disagreement between the ESG ratings of five prominent rating agencies. The paper proceeds to trace the disagreement to the most granular level of ESG categories that is available and decomposes the overall divergence into three sources: Scope divergence related to the selection of different sets of categories, measurement divergence related to different assessment of ESG categories, and weight divergence related to the relative importance of categories in the computation of the aggregate ESG score. We find that measurement divergence explains more than 50 percent of the overall divergence. Scope and weight divergence together are slightly less important. In addition, we detect a rater effect, i.e., the rating agencies' assessment in individual categories seems to be influenced by their view of the analyzed company as a whole. The results allow investors, companies, and researchers to understand why ESG ratings differ.

Applying Behavioral Finance to Investments: The Existence and Importance of 'Investment Tribes'
Muralidhar, Sid,Muralidhar, Arun
Investment organizations are complex to understand because decisions are the aggregation of multiple individuals who influence the process. This applies to organizations that apply both quantitative and qualitative investment approaches because the first step in a quantitative approach is still a qualitative statement of the investment hypothesis that is then formalized by the models. Increasingly, finance practice is starting to incorporate behavioral finance insights and recognize that individuals are not “rational utility maximizers”, but rather complex psychological beings that can exhibit non-traditional behaviors. Kahneman-Tversky (KT) introduced this notion of individuals being “humans” and not “econs” but made broad generalizations about behaviorally affected decisions (BADs) and did not provide a methodology to examine individual biases. A technique has been proposed to use KT’s questions to provide individual behavioral diagnostics and previous research demonstrated dramatic differences within and across groups based on age, gender and financial literacy, especially for gambles based on prospective gains and losses. This paper is a case study of a single asset management company that uses a mix of quantitative and qualitative investment techniques, driven by five decision makers with equally weighted votes in the final decision. In addition to demonstrating that each individual in the investment committee is unique and providing individual diagnostics that show that age, gender and education need not lead to a particular behavior, this paper highlights the fact that organizations have “investment tribes”; namely, that there are groups of individuals who appear to exhibit similar risk tendencies for gambles involving gains or losses. These tribes can influence and impact decision-making. Quantifying and making transparent the existence of these tribes could improve decision-making. This case study can be helpful for investment firms allowing them to become aware of potential biases, and also for asset owners that delegate decisions to third parties, as it allows them to understand how the investment firms they delegate to might behave when they experience drawdowns.

Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey
Rico Krueger,Taha H. Rashidi,Vinayak V. Dixit

Autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a consequence, the generalised cost of car travel may decrease, and car users may become less sensitive to travel time. By facilitating private motorised mobility, AVs may eventually impact land use and households' residential location choices. This paper seeks to advance the understanding of the potential impacts of AVs on travel behaviour and land use by investigating stated preferences for combinations of residential locations and travel options for the commute in the context of autonomous automobile travel. Our analysis draws from a stated preference survey, which was completed by 512 commuters from the Sydney metropolitan area in Australia, and provides insights into travel time valuations in a long-term decision-making context. For the analysis of the stated choice data, mixed logit models are estimated. Based on the empirical results, no changes in the valuation of travel time due to the advent of AVs should be expected. However, given the hypothetical nature of the stated preference survey, the results may be affected by methodological limitations.

Board Diversity, Director Dissent, and Monitoring Effectiveness
Kang , Jun-Koo ,Kim, Seil,Oh, Seungjoon
Using unique director voting data from Korean firms, we examine how board diversity affects the likelihood of director dissent and whether proposal rejection due to dissension affects firm value and policies. We find that directors on diverse boards are more likely to dissent. The result is robust to exploiting unexpected director resignations as exogenous variation in board diversity. Following proposal rejection, firms, particularly those with diverse boards, experience improvements in firm value and governance (lower earnings management, higher forced CEO turnover-performance sensitivity, and less investment inefficiency) and a decrease in risk, suggesting that board diversity enhances directors’ monitoring effectiveness.

ChainNet: Learning on Blockchain Graphs with Topological Features
Nazmiye Ceren Abay,Cuneyt Gurcan Akcora,Yulia R. Gel,Umar D. Islambekov,Murat Kantarcioglu,Yahui Tian,Bhavani Thuraisingham

With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. %explain higher order interactions among the nodes in Blockchain graphs and can be used to build much more accurate price prediction models. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.

Collateral and Asymmetric Information in Lending Markets
Ioannidou, Vasso,Pavanini, Nicola,Peng, Yushi
We study the benefits and costs of collateral requirements in bank lending markets with asymmetric information. We estimate a structural model of firms' credit demand for secured and unsecured loans, banks' contract offering and pricing, and firm default using detailed credit registry data in a setting where asymmetric information problems in credit markets are pervasive. We provide evidence that collateral mitigates adverse selection and moral hazard. With counterfactual experiments, we quantify how an adverse shock to collateral values propagates to credit supply, credit allocation, interest rates, default, and bank profits and how the severity of adverse selection influences this propagation.

Corporate Board Network Ties and Corporate Social Performance Alignment
Lin, KC,Dong, Xiaobo
This paper examines the effect of board composition on corporate social performance (CSP). Building on social network theory, this study hypothesizes that knowledge and experience of social practices diffuse across different firms through board members. This paper also explores the properties of the board and interlocking directors as moderating factors that affect diffusion of CSP. Empirical findings indicate that CSP of two firms becomes closely aligned when the firms appoint the same members on their respective boards. The ability of interlocking directors to facilitate knowledge transfer and the receptibility of the receiving board both affect the success of the social practice diffusion. This paper is the first to document the effect of board of directs’ social network on the focal firm’s CSP. Additional findings further highlight the importance of considering both the nature of the information being transferred and the nature of network ties when studying the impact of social networks on firm behaviors.

Counterparty Risk Allocation
Baule, Rainer
We address the problem of minimizing the risk of an exposure (e.g., cash holdings) to a small number of defaultable counterparties based on spectral risk measures, in particular the expected shortfall. The resulting risk-minimal allocation turns out to be economically implausible in a number of ways: When the loss distribution is discrete, only corner solutions can be optimal, and the optimization problem is ill-posed, as the risk-minimal allocation does not depend continuously on the input parameters. With two counterparties, only a total allocation to one counterparty or a fifty-fifty solution can be optimal. In general, the risk-minimal allocation is not monotonic in the quantile used for calculating the expected shortfall. This non-monotonicity also holds for continuous loss distributions. These results strengthen the doubts on the appropriateness of spectral risk measures in the target function for economic decision making.

Determinants of Corporate Governance Disclosures of Islamic Banks in Sudan: Implications for Shariah Governance
Sulub, Saed,Salleh, Zalailah,, Hafiza Aishah Hashim
The paper examines the association between the effectiveness of governance bodies in Islamic banks and Corporate Governance Disclosure (CGD) in a sample of Sudanese banks. We analysed the content of annual reports and employed Ordinary Least Squares (OLS) regression model with pooled effects. Consistent with previous studies in Islamic banks, the findings of this paper revealed low levels of CGD in Islamic banks of Sudan, which is only 39%, on average. The findings showed that Islamic banks with SSB members who hold advanced qualifications provided more information on CGD than their counterparts. However, we found that banks with SSB members who sit on more than one board tend to have lower CGD. In addition, we found that Islamic banks that have an established Audit Committee (AC), Internal Audit Function (IAF) and low levels of governmental ownership have higher CGD levels.These results are robust to alternative empirical models. Our study adds to the ongoing debate of Shariah governance in Islamic banks. In particular, while we support that IAF may play a significant role in Shariah governance as recommended by the regulators of the Islamic banking industry, our evidence shows that SSB multiple directorships, ceteris paribus, are not advantageous for Islamic banks.

Dynamic Financing with Imperfect Monitoring
Kakhbod, Ali,Li, Kevin
We study a contracting problem in continuous time where the principal hires an agent to conduct an R&D project for which progress towards success is binary. Under general concave payoffs, we explicitly derive the optimal dynamic incentive contract. In the first best scenario where incentives between the agent and principal are aligned, the optimal contract is constant. In contrast, when incentive compatability is a binding constraint, the optimal contract is explicitly characterized by the unique solution of an ordinary differential equation. The duration of employment is also uniquely specified by an endogenous threshold. The principal is patient near that threshold and his continuation value may in fact be negative in a neighborhood of the threshold. Importantly, due to the lumpy nature of the project completion, the optimal incentive-pay is two-dimensional: a flow payments during the R&D phase, and a lump-sum reward upon successful completion of the project. Finally, in numerical simulations, we find that the optimal contract features a miniscule level of flow payments, where most of the agent’s benefit come from the lump-sum reward when the project is successful. This theoretical feature of our model agrees with empirical evidence that CEO compensation is tied to the success of research agendas taking place over a long time horizon.

Explosion in the quasi-Gaussian HJM model
Dan Pirjol,Lingjiong Zhu

We study the explosion of the solutions of the SDE in the quasi-Gaussian HJM model with a CEV-type volatility. The quasi-Gaussian HJM models are a popular approach for modeling the dynamics of the yield curve. This is due to their low dimensional Markovian representation which simplifies their numerical implementation and simulation. We show rigorously that the short rate in these models explodes in finite time with positive probability, under certain assumptions for the model parameters, and that the explosion occurs in finite time with probability one under some stronger assumptions. We discuss the implications of these results for the pricing of the zero coupon bonds and Eurodollar futures under this model.

Foreign Currency Loan Conversions and Currency Mismatches
Fischer, Andreas M.,Yesin, Pinar
This paper examines the effect of currency conversion programs from Swiss franc-denominated loans to other currency loans on currency risk for banks in Central and Eastern Europe (CEE). Swiss franc mortgage loans proliferated in CEE countries prior to the financial crisis and contributed to the volume of non-performing loans as the Swiss franc strongly appreciated during the post-crisis period. Empirical findings suggest that Swiss franc loan conversion programs reduced currency mismatches in Swiss francs but increased bank exposure in other foreign currencies in individual countries. This asymmetric effect of conversion programs arises from the loan restructuring from Swiss francs to a non-local currency and the high level of euro mismatches in the CEE banking system.

How Do Private Digital Currencies Affect Government Policy?
Raskin, Max,Saleh, Fahad,Yermack, David
This paper provides a systematic evaluation of the different types of digital currencies. We express skepticism regarding centralized digital currencies and therefore focus our economic analysis on private digital currencies. Specifically, we highlight the potential for private digital currencies to improve welfare within an emerging market with a selfish government. In that setting, we demonstrate that a private digital currency not only improves citizen welfare but also encourages local investment and enhances government welfare. The fact that a private digital currency enhances government welfare implies a permissive regulatory policy which enables citizens to realize the previously referenced welfare gains.

Indirect Inference With(Out) Constraints
David T. Frazier,Eric Renault

Indirect Inference (I-I) estimation of structural parameters $\theta$ {{requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters $\beta$.}} {The estimators of the instrumental parameters will encapsulate} the statistical information used for inference about the structural parameters. As such, artificially constraining these parameters may restrict the ability of the auxiliary model to accurately replicate features in the structural data, which may lead to a range of issues, such as, a loss of identification. However, in certain situations the parameters $\beta$ naturally come with a set of $q$ restrictions. Examples include settings where $\beta$ must be estimated subject to $q$ possibly strict inequality constraints $g(\beta) > 0$, such as, when I-I is based on GARCH auxiliary models. In these settings we propose a novel I-I approach that uses appropriately modified unconstrained auxiliary statistics, which are simple to compute and always exists. We state the relevant asymptotic theory for this I-I approach without constraints and show that it can be reinterpreted as a standard implementation of I-I through a properly modified binding function. Several examples that have featured in the literature illustrate our approach.

Institutional Ownership and Corporate Governance of Public Companies in China
Guo, Lin,Platikanov, Stefan
Using information on the identity and percentage ownership of the ten largest shareholders in Chinese nonfinancial firms during the period of 1999â€"2010, we examine institutional investors' preferences for specific firm characteristics and investigate how institutional ownership affects firm value. We classify institutional investors under two dimensions: (1) pressure-sensitive (or grey) institutions versus pressure-insensitive (or independent) institutions, and (2) state-owned versus privately-owned institutions. We find that institutional ownership is positively associated with company Tobin's Q, and the effect is mainly driven by the ownership of independent institutions, rather than driven by the ownership of privately-owned institutions. Moreover, this positive relation gets stronger after 2005, when significant progress has been made in strengthening the legal and institutional foundation of the capital markets in China. In contrast, ownership by grey institutions is in general negatively associated with firm performance, manifesting the value-destructive consequences of the conflicts of interest these investors cultivate. The significant change after 2005 suggests that the enhanced external legal and corporate governance environment is critical for independent institutions' monitoring role to strengthen.

Jumps at Ultra-High Frequency: Evidence From the Chinese Stock Market
Zhang, Chuanhai,Liu, Zhi,Liu, Qiang
This paper proposes a new jump test for semi-martingale contained by microstructure noise based on the threshold pre-averaging bi-power estimation. Theoretically, we prove that such test has asymptotical size and power. Monte Carlo simulations show that the new test has better performance than Christensen et al(2014)'s test in noisy setting and we also consider adopting the false discovery rate (FDR) threshold technique to avoid spurious detections. In the empirical part, we investigate the contributions of jumps to total return variance from the Chinese stock market based on the tick-by-tick transaction data. The empirical results imply that the jump variation is an order of magnitude smaller than typical estimates found in the existing literature from different perspectives.

Minority Depository Institutions: Why So Few â€" After 150 Years?
Barth, James R.,Xu, Jiayi
In 1865, the first minority bank in the United States was established. Over time, depository institutions owned or controlled by minorities, known as minority depository institutions (MDIs), have grown in number. Yet, one hundred and fifty years later, they still account for only 2.8 percent of all banks. The contribution of this paper is twofold. First, we examine whether MDIs locate offices in lower-income communities and those that are predominantly minority. Second, we examine the role and performance of MDIs from the perspective of whether their disproportionately small role in the banking industry is due to their relatively poorer and riskier performance as compared to non-MDIs, controlling for the household-income level and poverty rate of the local communities in which they operate. Based on our empirical results, we find that a MDI is highly likely to be located in a community in which the largest share of the population is minority and one in which income and poverty are worse compared to national averages. When we consider both MDIs and non-MDIs located in the same communities, which controls for common factors affecting both types of banks, MDIs generally, in contrast to many previous studies, show no sign of underperformance or greater riskiness.

Persistent Government Debt and Aggregate Risk Distribution
Croce, Mariano (Max) Massimiliano,Raymond, Steve
When government debt is sluggish, consumption exhibits lower expected growth, more

Predicting Consumer Default: A Deep Learning Approach
Albanesi, Stefania,Vamossy, Domonkos
We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.

Production Efficiency and Financial Performance in Pharmaceutical Industry: A Case of Top 24 Companies
Hashemian-Rahaghi, Seyed-Reza,FanFah, Cheng,Abed-Ashtiani, Farnaz
Contributing to the field of medicine, pharmaceutical industry plays a key role in development of the society’s health, an extremely important social and economic asset. Current study is of crucial importance as it applied data envelopment analysis and traditional accounting indicators to measure the production efficiency and financial performance of top 24 pharmaceutical producers over five years (2011-2015). Based on the results, major portion of the tested companies showed production efficiencies and profitability positions below the industry average. Overall liquidity and asset utilization position of the industry also was not satisfying. Only two companies i.e. Novo Nordisk, a company from Denmark, and Gilead Sciences, an American company, indicated overall pleasing financial and efficiency performances. Allergan, an American company, revealed lower financial and efficiency results than other pharmaceutical producers. Japanese companies notably Takeda showed no satisfying results particularly in terms of profitability. Bayer, a company from Germany revealed better efficiency performance than its compatriot, Merck-KGaA. Comparing two Swiss companies, Roche outpaced Novartis. AbbVie and GlaxoSmithKline needed to reduce their extensive reliance on debt financing. Financial results of AstraZeneca, Sanofi, Pfizer, Merck & Co., Bristol-Myers-Squib, and Teva were below the industry average. Amgen showed an extensive cash tied up in non-productive assets.

Review Article: Financial Performance Measures in Merger and Acquisition Studies
Hashemian-Rahaghi, Seyed-Reza,FanFah, Cheng,Abed-Ashtiani, Farnaz,Hassan, Taufiq
Mergers and Acquisitions (M&A)s are motivated by various reasons and their real result is difficult to predict as many factors affect their outcome and procedure. Current study aimed to review M&A researches which used financial and accounting measures. A group of studies indicated that restructurings leaded to better financial and operating profile of companies and they resulted in synergistic gains. However, many researches concluded no significant improvement was achieved or restructurings worsened the performance of companies. Studies implied that rationale for unsuccessful M&As could be explained by agency conflicts or hubris theory. Studies also explained financial motivation as another factor inducing M&As especially during tough times or where environmental factors provided context for restructurings. Size of the merger participants and industry classification impacted the M&A procedure. However studies reported contradictory findings of effect of M&A type on restructurings and no relationship observed between post acquisition performance of companies and method of payment.

Risk-Free Interest Rates
Diamond, William,Grotteria, Marco,van Binsbergen, Jules H.
We estimate risk-free interest rates unaffected by convenience yields on safe assets. We infer them from risky asset prices without relying on any specific model of risk. We obtain a term structure of convenience yields with maturities up to 2.5 years at a minutely frequency. The convenience yield on treasuries equals about 40 basis points, is larger below 3 months maturity, and quadruples during the financial crisis. In high-frequency event studies, conventional and unconventional monetary stimulus reduce convenience yields, particularly during the crisis. We further study convenience-yield-free CIP deviations, and we show significant bond return predictability related to convenience yields.

Robonomics: The Study of Robot-Human Peer-to-Peer Financial Transactions and Agreements
Irvin Steve Cardenas,Jong-Hoon Kim

The concept of a blockchain has given way to the development of cryptocurrencies, enabled smart contracts, and unlocked a plethora of other disruptive technologies. But, beyond its use case in cryptocurrencies, and in network coordination and automation, blockchain technology may have serious sociotechnical implications in the future co-existence of robots and humans. Motivated by the recent explosion of interest around blockchains, and our extensive work on open-source blockchain technology and its integration into robotics - this paper provides insights in ways in which blockchains and other decentralized technologies can impact our interactions with robot agents and the social integration of robots into human society.

Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge
Alessio Arleo,Christos Tsigkanos,Chao Jia,Roger A. Leite,Ilir Murturi,Manfred Klaffenboeck,Schahram Dustdar,Michael Wimmer,Silvia Miksch,Johannes Sorger

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

Small-noise limit of the quasi-Gaussian log-normal HJM model
Dan Pirjol,Lingjiong Zhu

Quasi-Gaussian HJM models are a popular approach for modeling the dynamics of the yield curve. This is due to their low dimensional Markovian representation, which greatly simplifies their numerical implementation. We present a qualitative study of the solutions of the quasi-Gaussian log-normal HJM model. Using a small-noise deterministic limit we show that the short rate may explode to infinity in finite time. This implies the explosion of the Eurodollar futures prices in this model. We derive explicit explosion criteria under mild assumptions on the shape of the yield curve.

Stress Testing and Bank Lending
Shapiro, Joel D.,Zeng, Jing
Bank stress tests are a major form of regulatory oversight. Banks respond to the toughness of the tests by changing their lending behavior. Regulators care about bank lending; therefore, banks' reactions to the tests affect the tests' design and create a feedback loop. We demonstrate that stress tests may be (1) soft, in order to encourage lending in the future, or (2) tough, in order to deter excessive risk-taking in the future. There may be multiple equilibria due to strategic complementarity. Regulators may strategically delay stress tests. We also analyze bottom-up stress tests and banking supervision exams.

Tax Efficient Structures for Hedge Fund Investing
Lhabitant, Francois
We discuss the major structures available for hedge fund investing, and how different categories of investors may use these to reduce the risk of double taxation, or benefit from the characteristics inherent in hedge fund investments rather than owning the same assets directly.

The Effect of Visual Design in Image Classification
Naftali Cohen,Tucker Balch,Manuela Veloso

Financial companies continuously analyze the state of the markets to rethink and adjust their investment strategies. While the analysis is done on the digital form of data, decisions are often made based on graphical representations in white papers or presentation slides. In this study, we examine whether binary decisions are better to be decided based on the numeric or the visual representation of the same data. Using two data sets, a matrix of numerical data with spatial dependencies and financial data describing the state of the S&P index, we compare the results of supervised classification based on the original numerical representation and the visual transformation of the same data. We show that, for these data sets, the visual transformation results in higher predictability skill compared to the original form of the data. We suggest thinking of the visual representation of numeric data, effectively, as a combination of dimensional reduction and feature engineering techniques. In particular, if the visual layout encapsulates the full complexity of the data. In this view, thoughtful visual design can guard against overfitting, or introduce new features -- all of which benefit the learning process, and effectively lead to better recognition of meaningful patterns.

The Financial Development of London in the 17th Century Revisited: A View from the Accounts of the Corporation of London
Sussman, Nathan
We study an overlooked episode of financial development in England during the 17th century. We construct a novel, annual series of interest rates paid by the Corporation of London. We show that: interest rates declined by 350 basis points; Interest rates co-moved with Amsterdam: we attribute half of this decline to the integration of the capital markets of London and Amsterdam and half to the increase in London's financial market liquidity. The reduction of the usury rate lowered interest rates by 50 basis points in the 1650s. England's financial evolution and path towards modern growth date, therefore, to the 17th century.

The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment
Xiao, Tim
This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a risk-free value. Credit value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level.

The emergence of critical stocks in market crash
Shan Lu,Jichang Zhao,Huiwen Wang

In complex systems like financial market, risk tolerance of individuals is crucial for system resilience.The single-security price limit, designed as risk tolerance to protect investors by avoiding sharp price fluctuation, is blamed for feeding market panic in times of crash.The relationship between the critical market confidence which stabilizes the whole system and the price limit is therefore an important aspect of system resilience. Using a simplified dynamic model on networks of investors and stocks, an unexpected linear association between price limit and critical market confidence is theoretically derived and empirically verified in this paper. Our results highlight the importance of relatively `small' but critical stocks that drive the system to collapse by passing the failure from periphery to core. These small stocks, largely originating from homogeneous investment strategies across the market, has unintentionally suppressed system resilience with the exclusive increment of individual risk tolerance. Imposing random investment requirements to mitigate herding behavior can thus improve the market resilience.

Tokenized Securites and Commercial Real Estate
Smith, Julie,Vora, Manasi,Benedetti, Hugo,Yoshida, Kenta,Vogel, Zev
The following research investigates the application of security tokenization to commercial real estate assets. Primary research through interviews was conducted to uncover some of the most salient use cases and blockchain benefits for the space. The report explores three domains of blockchain application to real estate: (1) the application of blockchain to securities issuance and trading, (2) the application of blockchain to the real estate investment value chain, and (3) the application of blockchain to the representation of the physical assets themselves. Overall, we find that the value creation provided by tokenization can come in several layers, with some standalone benefits emerging by applying tokenization to each of the three domains in isolation. However, significant synergies can arise from combining these layers. As integration increases, additional features become possible. Our conclusion offers a general framework that can be used to perform future research on the tokenization of other types of assets and their related securities.

TÜRKİYE CUMHURİYET MERKEZ BANKASI (TCMB) REZERVLERİNİN GÜÇLENDİRİLMESİNE YÖNELİK BİR ÖNERİ: FİNANSAL ARACILARA REZERV VERGİSİ (A Recommendation for Strengthening Reserves of the Central Bank of the Republic of Turkey (CBRT): Reserve Tax to Financial Intermediaries)
Kartal, Mustafa Tevfik,Tan, Omer
Turkish Abstract: Son zamanlarda Türkiye’de döviz kurlarında aşarı dalgalanma görülmektedir. Bu nedenle döviz kurlarının istikrarlı hale getirilmesi için neler yapılması gerektiği en çok tartışılan konular haline gelmiştir. Bu kapsamda, kullanılabilecek önemli araçlardan birinin TCMB rezervleri olduğu dikkate alınarak TCMB rezervlerinin güçlendirilmesi amacıyla finansal kurumlara rezerv vergisi yükümlülüğü getirilmesi önerilmektedir. Bu çalışma ile söz konusu önerinin kapsamlı bir şekilde ele alınması amaçlanmaktadır.English Abstract: Excessive volatility in foreign exchange rates have been seen recently in Turkey. For this reason, they have become the most argued subjects that cause increase of foreign exchange rates in Turkey and what should be applied in order to make foreign exchange factors stable. In this context, taking into account that one of the important tools that can be used is CBRT reserves, it is recommended to bring reserve tax liability to financial institutions in order to increase CBRT’s reserves. With this study, it is aimed to examine the recommendation in detail.

What Matters When? Time-Varying Sparsity in Expected Returns
Bianchi, Daniele,Büchner, Matthias,Tamoni, Andrea
We provide a measure of sparsity for expected returns within the context of classical factor models. Our measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions and for liquidity supply are key determinants of the variability in sparsity. Deteriorating financial conditions and illiquid times are associated with an increase in the number of characteristics that are useful to predict anomaly returns (i.e., the forecasting model becomes more dense). Looking at specific categories of characteristics, we find that only trading frictions is robustly present throughout the sample. A substantial amount of the time-variation in sparsity is attributable to the value, profitability, and investment categories. A strategy that exploits the dynamics of sparsity to time factors delivers substantial economic gain out-of-sample relative to both a random walk and a model based on preselected, well-know characeristics like size, momentum and book-to-market.