Research articles for the 2019-12-18

A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field
Zachary Feinstein,Andreas Sojmark

In this work we introduce a model of default contagion that combines the approaches of Eisenberg-Noe interbank networks and dynamic mean field interactions. The proposed contagion mechanism provides an endogenous rule for early defaults in a network of financial institutions. The main result is to demonstrate a mean field interaction that can be found as the limit of the finite bank system generated from a finite Eisenberg-Noe style network. In this way, we connect two previously disparate frameworks for systemic risk, and in turn we provide a bridge for exploiting recent advances in mean field analysis when modelling systemic risk. The mean field limit is shown to be well-posed and is identified as a certain conditional McKean-Vlasov type problem that respects the original network topology under suitable assumptions.

A Generalized Heterogeneous Autoregressive Model using the Market Index
Hizmeri, Rodrigo,Izzeldin, Marwan,Nolte, Ingmar,Pappas, Vasileios
This paper shows that generalizing the heterogeneous autoregressive model (HAR) with realized (co)variances and semi-(co)variances from the index leads to more accurate volatility forecasts. To circumvent the effects of the market microstructure noise arising from using high sampling frequencies, we adopt noise-robust estimators for the realized (co)variances and develop novel noise-robust estimators for the semi-(co)variances. To explore the sampling frequency at which the forecasting gains are maximized, we adopt a mixed-sampling approach that iterates over several sampling frequencies of the stock and the index. Our analysis shows that gains are maximized at the combination of a low (high) frequency on the stock (index). We illustrate that the observed forecasting gains translates into economic gains such that a risk-averse investor is willing to pay up to 57 annual basis points by adopting a model specification that utilizes the index information.

Are Cryptocurrencies Homogenous?
Gustafsson, Frida,Bengtsson, Elias
This article investigates if the returns of cryptocurrencies are affected in similar ways by a selection of demand and supply-side determinants. These determinants include both economic determinants, such as macroeconomic factors and uncertainty, and technical determinants, such as token supply, distribution and transaction validation. Homogeneity among cryptocurrencies is tested via a LASSO-model in which the determinants of returns that have been identified in research on Bitcoin are applied to a sample of 12 cryptocurrencies. The analysis goes beyond much existing research by simultaneously covering different time periods and design choices of cryptocurrencies.The results show that cryptocurrencies are heterogenous, apart from some similarities in the impact of technical determinants. The cryptocurrency market is highly integrated with evidence of substitution effects. Further, design choices related to demand and supply among cryptocurrencies often explain the impact of determinants of return. It is important to consider heterogeneity among cryptocurrencies in order to enlighten the ongoing debates about the social contribution, economic advantages and risks, and potential regulation of cryptocurrencies.

Believing in Bail-In? Market Discipline and the Pricing of Bail-In Bonds
Lewrick, Ulf,Serena, José María,Turner, Grant
Bail-in regulation is a centrepiece of the post-crisis overhaul of bank resolution. It requires major banks to maintain a sufficient amount of "bail-in debt" that can absorb losses during resolution. If resolution regimes are credible, investors in bail-in debt should have a strong incentive to monitor banks and price bail-in risk. We study the pricing of senior bail-in bonds to evaluate whether this is the case. We identify the bail-in risk premium by matching these bonds with comparable senior bonds that are issued by the same banking group but are not subject to bail-in risk. The premium is higher for riskier issuers, consistent with the notion that bond investors exert market discipline on banks. Yet the premium varies pro-cyclically: a decline in marketwide credit risk lowers the bail-in risk premium for all banks, with the compression much stronger for riskier issuers. Banks, in turn, time their bail-in bond issuance to take advantage of periods of low premia.

Bitcoin: The Longest Running Mania â€" Tulips of the 21st Century?
Taskinsoy, John
Almost four centuries later, the Dutch tulipmania of the 17th century is always mentioned as a mania and used as a reference point in the aftermath of contemporaneous economic and financial crises since the late 1990s. Studies investigating what drove the tulip speculation throughout 17th and 18th centuries ignored market fundamentals, which we believe were the driving forces in Bitcoin price speculation and the ensuing crash. Bitcoin mania is far from a true madness, the increased frequency in its boom-and-bust cycle since 2017 comes from the cryptocurrency market’s extreme reactionary mode to any good or bad news from regulators and central banks (the Fed and ECB in particular) as well as security issues related to cyberattacks. In a matter of several months, the price of Bitcoin skyrocketed from $2,000 in April 2017 to the intraday high of $20,089 on December 17, 2017. The potential Bitcoin bubble occurred in the end of 2017, Bitcoin price surged from $5,600 to $20,089 in October â€" December 2017 and crashed in January 2018.

Building Financial Resilience Through Financial and Digital Literacy in South Asia and Sub-Saharan Africa
Lyons, Angela,Kass-Hanna, Josephine,Liu, Fan,Greenlee, Andrew,Zeng, Lianyun
Globally, 1.7 billion adults still lack access to formal financial services. Most of these financially excluded individuals (over 75% of the adult population) are living in South Asia and Sub-Saharan Africa. Those populations most vulnerable include the poor, those living in rural areas, and women. Currently, financial inclusion strategies go beyond providing access to bank accounts and include a more complex array of financial services that aim to build more inclusive and financially resilient societies. With the movement towards fintech and over 80% of the world’s population having a mobile phone, digital financial literacy is gaining momentum. Yet, little research has attempted to rigorously measure the impacts of digital literacy on financial behavior, especially compared to and in conjunction with financial literacy. This study uses data from the InterMedia Financial Inclusion Insights (FII) surveys from seven South Asian and Sub-Saharan African countries to investigate the impact of multi-dimensional measures of financial and digital literacy on resilience-building financial behaviors â€" including savings, borrowing, risk management and insurance. Robustness checks are conducted for economically vulnerable populations to identify heterogeneities and inequality gaps. Two-stage models using instrumental variables are also included to check for endogeneity. The findings show that both financial and digital literacy are key factors to building inclusiveness and financial resilience. The results are consistent and significant across all models. The findings emphasize the need to redefine traditional financial literacy to include digital literacy and have important implications for countries considering both financial and digital literacy as a dual approach to improving households’ long-run financial resilience.

De Jure Benchmark Bonds
Remolona, Eli,Yetman, James
Benchmark bonds help to improve market efficiency. They seem to arise spontaneously in deep and liquid markets. Can governments help to create them where markets are too small? This paper examines three emerging markets in Asia where authorities have tried: they have designated specific bonds as benchmarks and fostered their liquidity. We identify exactly which bonds were the designated benchmarks. We then propose rank-order measures of liquidity and determine the extent to which these de jure benchmarks end up as de facto benchmarks in the sense of being the most liquid bonds in their maturity segments. We find that this occurs in close to 60% of months in our sample, covering a range of maturities for Indonesia, Malaysia and Thailand. We identify three factors that make success more likely: (a) choosing already liquid bonds; (b) choosing bonds that have previously served as de jure benchmarks; and (c) choosing bonds that will be issued during the month.

ESG Performance and Disclosure: A Cross-Country Analysis
Lopez de Silanes, Florencio,McCahery, Joseph A.,Pudschedl, Paul
We use a unique dataset to examine the link between ESG disclosure and quality through a cross-country comparison of disclosure requirements and stewardship codes. We find a strong relationship between the extent of ESG disclosure and the quality of a firm’s disclosure. Furthermore, we find that ESG is correlated with decreased risk. This result suggests that firms with good ESG scores are simply disclosing more information. Finally, we show that ESG scores have little or no impact on risk-adjusted financial performance.

Economics of disagreement -- financial intuition for the R\'enyi divergence
Andrei N. Soklakov

Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that -- we want disagreements to be resolved. This leaves us with a substantial knowledge gap which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts.

Take for instance the R\'enyi divergence which is a well-known statistical quantity specifically designed as a measure of disagreement between probabilistic models. Despite its widespread use in science and engineering, the R\'enyi divergence remains a highly abstract axiomatically-motivated measure. Certainly, it offers no practical insight as to how disagreements can be resolved.

Here we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. The R\'enyi divergence appears connected to the optimized financial performance. The optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning future climate).

In terms of scientific validation, we use findings of independent neurophysiological experiments as well as our own research on the equity premium.

Examining the Impact of Divestment from Fossil Fuels on University Endowments
Ryan, CJ,Marsicano, Christopher
Between 2011 and 2018, 35 American universities and colleges divested, either partially or completely, their endowments from fossil-fuel holdings, marking a shift toward sustainability in university endowment investment. However, the decision by these universities to divest their endowments from fossil-fuel holdings was often marked by controversy, owing to conflicts between student- and faculty-led coalitions and the university board, and conflicting interpretations of fiduciary law â€" both of which are addressed in this Article. To date, the academic study of the effect of divestment on endowment values has focused on the top university endowments and has produced mixed results. With respect to mixed results, our study is no different. However, our study is different from the extant but limited literature in this area in that we examine holistically the impact of total or partial divestment on endowment values for all universities as well as a select group of institutions that are illustrative of their peers by endowment size. Results from our difference-in-differences analyses of the effect of full and partial divestment suggest that either form of divestment yields negative consequences for endowment values. We urge the reader to interpret these preliminary results with caution, in part because our second method of analysis, using the synthetic control method for four universities (Pitzer College, Stanford University, University of Dayton, and Syracuse University), suggests that the negative consequences of divestment may be overstated in the near-term. Specifically, these results suggest that there is not a negative effect associated with divestment for mid-sized and large endowments. We hope that this study both grounds and advances the debate about endowment divestment with empirical evidence and a reasoned discussion of its costs and benefits.

Extremal dependence and spatial risk measures for insured losses due to extreme winds
Erwan Koch

A meticulous assessment of the risk of impacts associated with extreme wind events is of great necessity for populations, civil authorities as well as the insurance industry. Using the concept of spatial risk measure and related set of axioms introduced by Koch (2017, 2019), we quantify the risk of losses due to extreme wind speeds. The insured cost due to wind events is proportional to the wind speed at a power ranging typically between 2 and 12. Hence we first perform a detailed study of the correlation structure of powers of the Brown-Resnick max-stable random fields and look at the influence of the power. Then, using the latter results, we thoroughly investigate spatial risk measures associated with variance and induced by powers of max-stable random fields. In addition, we show that spatial risk measures associated with several classical risk measures and induced by such cost fields satisfy (at least part of) the previously mentioned axioms under conditions which are generally satisfied for the risk of damaging extreme wind speeds. In particular, we specify the rates of spatial diversification in different cases, which is valuable for the insurance industry.

Forecasting significant stock price changes using neural networks
Firuz Kamalov

Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multi-layer perceptron, convolutional net, and long short term memory net. As benchmark models we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change.

Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery
Andrey Itkin,Fazlollah Soleymani

In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. First, it is known since the Lehman Brothers bankruptcy that the recovery rate could significantly vary right before or at default, therefore, in this paper we consider it to be stochastic. Second, to reduce complexity of the model, we treat the domestic interest rate as deterministic, because, as shown in ISV2019, volatility of the domestic interest rate does not contribute much to the value of the Quanto CDS spread. Finally, to solve the corresponding systems of 4D partial differential equations we use a different flavor of the Radial Basis Function (RBF) method which is a combination of localized RBF and finite-difference methods, and is known in the literature as RBF-FD. Results of our numerical experiments presented in the paper demonstrate that the influence of volatility of the recovery rate is significant if the correlation between the recovery rate and the log-intensity of the default is non-zero. Also, the impact of the recovery mean-reversion rate on the Quanto CDS spread could be comparable with the impact due to jump-at-default in the FX rate.

Moments of Student's t-distribution: A Unified Approach
Kirkby, Justin,Nguyen, Dang,Nguyen, Duy
In this paper, we derive the closed form formulae for moments of Student's t-distribution in the one dimensional case as well as in higher dimensions through a unified probability framework. Interestingly, the closed form expressions for the moments of Student's t-distribution can be written in terms of the familiar Gamma function, Kummer's confluent hypergeometric function, and the hypergeometric function.

Monitoring of Fair Value Reliability by Third-Party Specialists: A Review and Integration of Empirical Research
Kjellevold, Kyrre
This paper reviews the academic literature on the effectiveness of third-party specialists in monitoring the reliability of fair value measurements (FVMs). Research indicates that companies’ use of third-party specialists to provide FVMs reduces investors’ information risk. Management may lack the necessary valuation expertise for measuring fair values and has been shown to provide biased FVMs. The use of a third-party specialist intends to compensate for these deficiencies. By integrating findings in the accounting, economics, and finance literature, the review provides novel insights into the monitoring role of third-party specialists and suggests directions for future research. Overall; the literature shows that third-party specialists are associated with more reliable FVMs across both financial and nonfinancial assets. This supports that specialists have an important monitoring role to secure reliable FVMs. Next, research suggests that the effectiveness of specialists as monitors is moderated by specialists’ economic incentives and by client pressure to inflate FVMs. Further, the monitoring effectiveness of specialists interacts with corporate governance mechanisms such as board independence. Finally, research shows that the financial statement audit only marginally affect the reliability of FVMs above the contribution of third-party specialists. Future research will benefit from a better understanding of specialists’ economic incentives and how these influence specialists’ monitoring effectiveness, as well as investigate how specialists’ monitoring role interacts with relevant governance mechanisms.

Portfolio Strategies for Volatility Investing
Campasano, Jim
The VIX premium has been shown to hold predictive power over volatility returns and investment risk. Applied within a portfolio construct, this study proposes a conditional strategy which allocates to market and volatility risk. While the strategy is predominantly short volatility, the strategy owns volatility during much of the financial crises. Both long and short volatility allocations prove profitable over the sample period, producing a portfolio more consistently profitable than the S\&P 500 Index and related strategies.

Telekom Malaysia Berhad: A Study of Relationship Between Performance (ROA) And Internal and External Factors
Mohd Shafarin, Nur Aisyah
The study’s aim is an attempt to determine the altogether performance of Telekom Malaysia Berhad which involved two main factors of internal (firm-specific) and external (macroeconomics) factors of Telekom Malaysia. This data was interpreted and collected Telekom Malaysia annual reports of five year period from 2014 to 2018. There are four risks involved which are liquidity risk, credit risk, operational risk, and market risk. Measurement of current ratio, quick ratio, average-collection period, debt to income ratio, operational ratio, and operating margin are used to examine the overall five years performance of Telekom Malaysia. Hence, to determine the relationship of these risk factors to the company’s performance, this study used liquidity risk, credit risk, operational risk, market risk, gross domestic products (GDP), inflation, interest rate, exchange rate, BETA, and corporate governance index. SPSS system is used to do data analysis in which by implementing step-wise method which applies the descriptive statistics, correlation, and model summary. Based on the data analysis, we can conclude that operational risk is the most significant to ROA since it gives the highest impact on performance of the company. Nonetheless, the other variables give low impact on the ROA and there is no significant related with.

The Affect of Operational Ratio on Corporate Governance of Emirates Nbd in United Arab Emirates (UAE)
Ramli, Nur Nabilah Syafiqah
This study investigates the interdependence between the risks in the presence of corporate governance in banking sector that listed in United Arab Emirates (UAE). There are a few variables used to determine the risks and the performance of the selected bank over five-year period of annual report from 2014 to 2018. This study utilized the main risks associated with the selected bank namely corporate governance, credit risk, operational risk, liquidity risk and market risk against the bank’s performance. These entire ratios will determine the performance associated with Emirates National of Dubai from the year 2014 to 2018. All the data collected is analyse by using IBM Social Science Statistical Package (SPSS) version 25.

The Anatomy of Trading Algorithms
Beason, Tyler,Wahal, Sunil
We study the anatomy of four widely used standardized institutional trading algorithms representing $675 billion in demand from 961 institutions between 2012 and 2016. The central tradeoff in these algorithms is between the desire to trade and transaction costs. Large parent orders generate hundreds of child orders which strategically employ the price, time, and display priority rules embodied in market structure to navigate this tradeoff. The distribution of child orders is non-random, generating strategic runs which oscillate between providing and taking liquidity. Price impact occurs both at the time an order is submitted to the book (regardless of whether it is filled), and at the time of execution. Passive child orders have much lower likelihood of execution but still incur substantial price impact. Conversely, marketable orders, even though immediately executable, do not necessarily guarantee execution and generate even larger price impact.

The Better Turbulence Index? Forecasting Adverse Financial Markets Regimes with Persistent Homology
Baitinger, Eduard,Flegel, Samuel
Persistent homology is the workhorse of modern topological data analysis, which in recent years becomes increasingly powerful due to methodological and computing power advances. In this paper, after equipping the reader with the relevant background on persistent homology, we show how this tool can be harnessed for investment purposes. Specifically, we propose a persistent homology based turbulence index for the detection of adverse market regimes. With the help of an out-of-sample study, we demonstrate that investment strategies relying on a persistent homology based turbulence detection outperform investment strategies based on other popular turbulence indices. Additionally, we conduct a stability analysis of our findings. This analysis confirms the results from the previous out-of-sample study, as the outperformance prevails for most configurations of the respective investment strategy and hence mitigating possible data mining concerns.

The Design of Digital Financial Infrastructure: Lessons from India
D’Silva, Derryl,Filková, Zuzana,Packer , Frank,Tiwari, Siddharth
Digital finance has the potential to transform emerging market and advanced economies alike. India's approach rests on the principle of providing digital financial infrastructure as a public good. It offers an important case study where the results are relevant and applicable for all economies, irrespective of their stage of development. The provision of a national digital biometric identity to all residents has effectively granted them broad access to the banking system. The development of a real-time payment system platform has brought efficiencies to retail customers and small-scale transactions. By providing cheap and instantaneous payment services to ordinary citizens, the design of the Indian payment system challenges the business case for standalone private payment systems. The establishment of a legal framework for data fiduciaries promises to ensure that individuals can readily access the data generated by their online activity and dictate the circumstances for sharing those data.

The Determinants of Credit Risk in Tiong Nam Logistics Holdings Berhad
Ab. Bari, Nur Anis Syazwani
The study examined the impact of internal and external factors towards credit risk in Tiong Nam Logistics Holdings Berhad. Debt to Income was used as dependent variable represented the credit risk. Debt to Income was calculated by total liability divided by total income. This study obtained time series regression analysis of five years from 2014 to 2018. The findings resulted debt to equity is the most significant variable to debt to income which influenced credit risk of the company. Based on the result for Model 1, debt to equity recorded the moderate significant and positively influenced to debt to equity when only the internal factors were tested. Whereas, when external factors were examined in Model 2 there was no variable giving significant influenced to debt to income. Meanwhile, for Model 3, there was only debt to equity has statically significant and positively influenced to debt to income out of all variables when internal and external factors were tested together. Thus, debt to equity is the most significant variable to debt to income which will arise the credit risk in Tiong Nam Logistics Holdings Berhad.

The ESG Sacrifice
Fish, Alexander Joshua Michael,Kim, DH,Venkatraman, Shankar
Socially Responsible investing has become a key point of discussion within the past few years as society gravitates towards producing positive externalities. The topic remains greatly contested especially when considering the perspectives of Milton Friedman, who claimed the sole focus of any publicly-traded company is to generate positive growth and performance for its shareholders. One of the key questions this paper sought to explore was whether or not an investor would sacrifice a portfolio’s performance in order to achieve a socially responsible portfolio. ESG (Environmental, Social, and Governance) Scores from Bloomberg and historical performance on various securities in both the United States and Europe were collected in order to construct various portfolios. For some portfolios, the returns were adjusted based on ESG Scores in order for companies with better ESG scores to receive more weight when portfolio optimization techniques were applied. In addition, given that the returns of companies with better ESG scores tended to have highly correlated returns, alternative portfolio optimization techniques beyond the traditional Mean-Variance Optimization were used to enhance the portfolio’s performance. Ultimately, it was shown that minimal difference existed between the returns of the ESG-weighted portfolios and the non-weighted portfolios indicating an investor does not have to sacrifice financial performance to achieve a socially responsible investment portfolio.

Uncovering Momentum
Malitskaia, Yulia
The explanation of the momentum premium represents an ongoing challenge, triggering the development of multiple risk-based and behavioral models. The paper explores the momentum strategy following a systematic divide-and-conquer approach composed from a sequence of top-bottom steps: dissecting the momentum performance along bull/bear states and winners/losers deciles; identifying the unscaled momentum decile as a basic common block across conventional, time-series and dual momentum strategies; rolling the combined in- and out-of-sample analysis, and clustering momentum decile time series. The corresponding findings support the Efficient Market Hypothesis and compliment existing models with techniques for identifying and assessing temporal patterns.