# Research articles for the 2019-06-03

A dynamic dual representation of the buyer's price of American options in a nonlinear incomplete market
Roxana Dumitrescu
arXiv

In this paper we study the problem of nonlinear pricing of an American option with a right-continuous left-limited (RCLL) payoff process in an incomplete market with default, from the buyer's point of view. We show that the buyer's price process can be represented as the value of a stochastic control/optimal stopping game problem with nonlinear expectations, which corresponds to the maximal subsolution of a constrained reflected Backward Stochastic Differential Equation (BSDE). We then deduce a nonlinear optional decomposition of the buyer's price process. To the best of our knowledge, no dynamic dual representation (resp. no optional decomposition) of the buyer's price process can be found in the literature, even in the case of a linear incomplete market and brownian filtration. Finally, we prove the "infimum" and the "supremum" in the definition of the stochastic game problem can be interchanged. Our method relies on new tools, as simultaneous nonlinear Doob-Meyer decompositions of processes which have a $\mathscr{Y}^\nu$-submartingale property for each admissible control $\nu$.

A simple and efficient numerical method for pricing discretely monitored early-exercise options
Min Huang,Guo Luo
arXiv

We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based on a quadrature technique, and it employs only elementary calculations and a fixed one-dimensional uniform grid. The convergence rate is $O(1/N^4)$ and the complexity is $O(MN\log N)$, where $N$ is the number of grid points and $M$ is the number of observation dates.

Ambiguity and Information Processing in a Model of Intermediary Asset Pricing
Han, Leyla Jianyu,Kasa, Kenneth,Luo, Yulei
SSRN
This paper incorporates ambiguity and information processing constraints into a model of intermediary asset pricing. Financial intermediaries are assumed to possess greater information processing capacity. Households purchase this capacity, and then delegate their investment decisions to intermediaries. As in He and Krishnamurthy (2012), the delegation contract is constrained by a moral hazard problem, which gives rise to a minimum capital requirement. Both agents have a preference for robustness, reflecting ambiguity about asset returns (Hansen and Sargent (2008)). We show that ambiguity aversion tightens the capital constraint, and amplifies its effects.Indirect inference is used to calibrate the modelâ€™s parameters to the stochastic properties of asset returns. Detection error probabilities are used to discipline the degree of ambiguity aversion. The model can explain both the unconditional moments of asset returns and their state dependence, even with DEPs in excess of 15%.

Analysis of Markov Chain Approximation for Diffusion Models with Non-Smooth Coefficients
Zhang, Gongqiu,Li, Lingfei
SSRN
In a recent work, Zhang and Li (2019, Operations Research) established sharp convergence rates of continuous-time Markov chain (CTMC) approximation for 1D diffusion models with smooth coefficients but non-smooth payoff functions, and proposed grid design principles to ensure nice convergence behaviors. However, their theoretical analysis fails to obtain sharp convergence rates when model coefficients or discounting factor lack smoothness. Moreover, it is unclear how to design the grid of CTMC to remedy the inferior convergence behaviors resulting from non-smooth model coefficients and discounting factor. In this paper, we introduce new ways for the theoretical analysis of CTMC approximation for general diffusion models with non-smoothness. We prove that convergence orders of value function and its first and second spatial derivatives are only first in general. However, strikingly, if all the discontinuous points are in the midway between two grid points (midpoint rule), second order convergence in the maximum norm is restored. Numerical examples including occupation time derivatives, shadow rate models and threshold models confirm the theoretical results and that the midpoint grid design rule can be generalized to 2D models. Surprisingly, though CTMC approximation for the problems under consideration is equivalent to a inconsistent finite difference scheme in our setting, its numerical performance is superior to a consistent one that imposes smooth pasting conditions at discontinuities.

Artificial Intelligence and Big Data in Entrepreneurship: A New Era Has Begun
Martin Obschonka,David B. Audretsch
arXiv

While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., 'smart entrepreneurship')? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.

Asset Pricing with General Transaction Costs: Theory and Numerics
Gonon, Lukas,Muhle-Karbe, Johannes,Shi, Xiaofei
SSRN
We study risk-sharing equilibria with general convex costs on the agents' trading rates. For an infinite-horizon model with linear state dynamics and exogenous volatilities, the equilibrium returns mean-revert around their frictionless counterparts - the deviation has Ornstein-Uhlenbeck dynamics for quadratic costs whereas it follows a doubly-reflected Brownian motion if costs are proportional. More general models with arbitrary state dynamics and endogenous volatilities lead to multidimensional systems of nonlinear, fully-coupled forward-backward SDEs. These fall outside the scope of known wellposedness results, but can be solved numerically using the simulation-based deep-learning approach of Han, Jentzen and E (2017). In a calibration to time series of returns, bid-ask spreads, and trading volume, transaction costs substantially affect equilibrium asset prices. In contrast, the effects of different cost specifications are rather similar, justifying the use of quadratic costs as a proxy for other less tractable specifications.

Benchmarking Collateral of Triple-A Rated Securities
Sarmiento, Camilo
SSRN
This paper presents a simple method to estimate the collateral associated with a Aaa tranche. The method is similar to historical simulation in the sense that there are no specific distributional assumptions, and the data fully determine the characteristics of the distribution. Both the transparency and simplicity of our method provide a valuable benchmark to existent tail of the distribution modeling. As a benchmark, our method also serves to validate collateral estimates for Aaa-rated securities as well as to validate capitalization models of financial institutions.

Bursting the Bitcoin Bubble: Assessing the Fundamental Value and Social Costs of Bitcoin
Podhorsky, Andrea
SSRN
This paper develops a microeconomic model of bitcoin production to analyze the economic effects of the Bitcoin protocol. I view the bitcoin as a tradable commodity that is produced by miners and whose supply is managed by the protocol. The findings show that bitcoinâ€™s volatile price path and inefficiency are related, and that both are a consequence of the protocolâ€™s system of supply management. I characterize the fundamental value of a bitcoin and demonstrate that the return on bitcoin appreciates proportionally to the rate of increase in the level of difficulty. In the model, where the price of a bitcoin is based on marginal production costs, successive positive demand shocks result in a rapidly increasing price path that may be mistaken for a bubble. The generalized supremum augmented Dickey-Fuller (GSADF) test is used to demonstrate that the model is able to account for the explosive behavior in the bitcoin price path, providing strong evidence that bitcoin is not a bubble. I also show that the difficulty adjustment mechanism results in social welfare losses from 17 March 2014 to 13 January 2019 of \$323.8 million, which is about 9.3% of the minersâ€™ total electricity costs during this time period.

Claim Models: Granular and Machine Learning Forms
Taylor, Greg
SSRN
The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development.This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving.However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models.Concluding sections briefly consider the possible further development of these models in the future.

Conditional inference on the asset with maximum Sharpe ratio
Steven E. Pav
arXiv

We apply the procedure of Lee et al. to the problem of performing inference on the signal noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used approximate standard error of the Sharpe ratio to use in this conditional estimation procedure. Testing indicates this procedure achieves the nominal type I rate, and does not appear to suffer from non-normality of returns. The conditional estimation test has low power under the alternative where there is little spread in the signal noise ratios of the assets, and high power under the alternative where a single asset has high signal noise ratio.

Corporate Fraud, Risk Avoidance, and Housing Investment in China
Niu, Geng,Yu, Li ,Fan, Gang-Zhi,Zhang, Donghao
SSRN
This study investigates the impact of corporate fraud on household investment choices. We conjecture that by undermining trust in the financial and insurance market, corporate fraud behaviour would decrease householdsâ€™ investment in risky financial assets and increase investment in non-financial assets. Combining data on householdsâ€™ investment behaviour with information on fraudulent activities of listed companies in China, we find that households with more lifetime experience of corporate fraud invest less in stocks and are less likely to purchase private insurance. By contrast, fraud experience increases householdsâ€™ intention to invest in residential real estate. In addition, the impact of corporate fraud is stronger among individuals who pay more attention to economic information, among individuals who have more social interaction, and among individuals in better regulated regions. Furthermore, we find that different types of fraud have differentiated effects on household investment decisions. Our findings indicate that corporate misconduct could generate profound negative externalities on the whole financial system.

Democracy and Economic Development: Disentangling the Effect of Elections and Rule of Law
Bakkar, Yassine,Ogcem, Ali Recayi
SSRN
Researchers continue to debate whether the various aspects of democracy affect the economic development. Investigating this relationship is still an important on-going question in the economy policy at the international level. This paper provides evidence on how the political settlements: rule of law and elections, would affect the economic development and enhances the economic growth, using data for 192 countries over the 2010-2012 period. This paper empirically investigates whether democracy has an impact on the economic convergence of countries through the quality of institutions, which are measured by the electoral component of democracy and rule of law parameters. Investigations differentiate between Islamic and non-Islamic countries (mainly European). Results indicate that the elections parameter has a first-order effect on economic development but such a relationship is not confirmed for Islamic countries. Rule of low also influence this relation, but bring less efficient impact to the economic development.

Do 'Speed Bumps' Prevent Accidents in Financial Markets?
Goncalves, Jorge,KrÃ¤ussl, Roman,Levin, Vladimir
SSRN
Is it true that speed bumps level the playing field, make financial markets more stable and reduce negative externalities of high frequency trading (HFT) firms? We examine how the implementation of a particular speed bump - Midpoint Extended Life order (M-ELO) on Nasdaq impacted financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high frequency order book message data around the implementation date and apply difference-in-differences analysis to estimate the average treatment effect of the speed bump on market stability and liquidity provision. The results suggest that the introduction of the M-ELO decreases the average number of crashes on Nasdaq compared to other exchanges by 2.7 per a hundred stocks. Liquidity provision by HFT firms also improves. These findings imply that technology-based solutions by exchanges are feasible alternatives to regulatory intervention towards safer markets.

Earnings Management in Domestic and Foreign IPOs in the United States: Do Home Country Institutions Matter?
Filatotchev , Igor,Jona, Jonathan,Livne, Gilad
SSRN
Using a large sample of domestic and foreign IPOs in the US, we investigate how threats of enforcement by the Securities and Exchange Commission (SEC) and private litigation influence earnings management in IPO prospectuses. We propose that perceptions of foreign institutions may influence SEC enforcement action and private litigation. We provide evidence that enforcement and litigation threats are negatively related to the strength of legal institutions in the foreign IPOâ€™s country of origin. We find earnings management is more pronounced in foreign IPOs from countries with strong legal institutions. We further explore whether earnings management is priced in the IPO market and find no relation between IPO proceeds and earnings management. Our results are consistent with upward earnings management as in Stein (1989), the magnitude of which is reduced when the anticipated cost of enforcement and litigation is higher. Collectively, our results cast doubt on the validity of the bonding hypothesis.

Enhancing Stock Movement Prediction with Adversarial Training
Fuli Feng,Huimin Chen,Xiangnan He,Ji Ding,Maosong Sun,Tat-Seng Chua
arXiv

This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model. The rationality of adversarial training here is that the input features to stock prediction are typically based on stock price, which is essentially a stochastic variable and continuously changed with time by nature. As such, normal training with static price-based features (e.g. the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose to add perturbations to simulate the stochasticity of price variable, and train the model to work well under small yet intentional perturbations. Extensive experiments on two real-world stock data show that our method outperforms the state-of-the-art solution with 3.11% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task.

Estimating Extreme Cancellation Rates In Life Insurance
Biagini, Francesca,Huber, Tobias,Jaspersen, Johannes Gerd,Mazzon, Andrea
SSRN
Extreme cancellation rates can severely distort life insurers' liquidity and profitability. Due to the rarity of the event and the complexity of policyholder behavior, the risk assessment of such a scenario is difficult. We introduce an estimation method that can utilize panel data on the company level to estimate the probability distribution function of a mass cancellation event as long as this function is continuous. The panel structure is taken into account by including annual fixed effects and company-level covariates. We demonstrate the method using cancellation rates from U.S. life insurers. We also apply it to German data and reveal difficulties in the European insurance regulation framework Solvency II. Our method allows risk managers and regulators to estimate extreme cancellation rates. In particular, we discuss the (in-)adequacy of the mass lapse scenario assumed in Solvency II, which can lead companies to have solvency capital requirements in the hundreds of millions.

Estimating Stochastic Volatility Model with Realized Volatility and Implied Volatility
Zhang, Zehua,Zhao, Ran
SSRN
This paper extends the Realized Stochastic Volatility model by jointly modeling return, realized volatility and option-implied volatility in one stochastic volatility (SV) framework. We examine how implied volatility, along with realized volatility, enhances the estimation on latent volatility process based on the single-move sampler of latent volatility for both the equity index ETF and individual stocks. Moreover, we compare the predictability of proposed implied volatility based SV model with benchmark realized volatility based SV models and find that the proposed SV model improves one-day ahead return density forecasts. Strong empirical evidences convince that incorporating implied volatility into SV framework ameliorates the prediction performance on index and stock return densities.

Financial Contracting as Behavior Towards Risk: The Corporate Finance of Business Cycles
Krainer, Robert
SSRN
In this paper I develop a model of a debt and equity financed representative firm whose production/investment decisions and financing decisions characterize business cycles. The conflict of interest problem between the differentially risk averse debt and equity investors is resolved with an optimal financial contract that in turn shapes the relationship between certain real and financial facts of business cycles ignored in Classical and Keynesian models of the business cycle. A no-arbitrage equilibrium in the product/factor market and financial market is attained when firms make production/investment decisions conforming to the risk aversion of stockholders (as reflected in stock valuations), and then make financing decisions to offset any risk shifting between bondholders and stockholders resulting from the production/investment decision. In this way the welfare of bondholders and stockholders is coalesced over the business cycle. The model is also used to describe employment patterns over the business cycle between young less risk averse apprentice workers and senior more risk averse workers protected with seniority.

Fintech and Financial Literacy in the Lao PDR
Morgan, Peter,Trinh, Long Q.
SSRN
A growing literature has examined the role of financial literacy in an individualâ€™s income, saving behavior and the use of various financial products. However, so far, no one has examined the relationship between financial literacy and the awareness and adoption of financial technology (fintech) products, i.e., financial products provided via internet-based and mobile-based platforms. This paper examines this relationship in a developing country, the Lao Peopleâ€™s Democratic Republic (PDR). We use information collected in the Lao PDR using the standardized questionnaire developed by the Organization for Economic Cooperation and Development International Network on Financial Education (OECD/INFE) to calculate our financial literacy. We find that a higher level of financial literacy has strong and positive effects on an individualâ€™s awareness of fintech products. This result still holds when we use a set of instrumental variables for the financial literacy variable. However, there is insufficient data to find a significant relationship between financial literacy and the use of fintech products.

Funding Fragility And Commercial Bank Failures
Antoniades, Adonis
SSRN
The paper examines the relation between funding fragility and the failures of commercial banks in the U.S. after the onset of the Great Recession. I do not find a strong empirical link between liquidity pressures unrelated to fundamentals and bank failures. Commercial banks' funding structures remained remarkably stable during the funding phase of the crisis. Aggregate shifts in funding costs can be explained almost in their entirety by shifts in policy rates, and result in lower than pre-crisis costs at the time when banks are failing. Furthermore, idiosyncratic funding spreads between failed and non-failed banks can be explained away by differences in solvency risk. Importantly, the trajectory of funding costs for non-core liabilities does not differ from that of core deposits in a manner that could draw a plausible link between funding fragility and bank failure rates.

Hedge Fund Strategies, Performance & Diversification: A Portfolio Theory & Stochastic Discount Factor Approach
Newton, David,Platanakis, Emmanouil,Stafylas, Dimitrios,Sutcliffe, Charles,Ye, Xiaoxia
SSRN
Using data on 5,500 North American hedge funds following 11 different strategies, we analyse the stand-alone performance of investing in these different strategies using a stochastic discount factor approach. Employing the same data, we then consider the diversification benefits of each hedge fund strategy when combined with a portfolio of US equities and bonds. We do this by computing the out-of-sample Black-Litterman and Bayes-Stein portfolios. All but two hedge fund strategies out-perform the market as stand-alone investments; and all but one provide significant diversification benefits. The higher is an investorâ€™s risk aversion, the more beneficial is diversification into hedge funds.

Heterogeneous Interpretations of Public Disclosure, Price Efficiency, and Keynesian Beauty Contest
Corona, Carlos,Wu, Sang
SSRN
This paper examines how public information about the fundamental value of a firm affects the firmâ€™s stock price behavior and efficiency in the presence of Keynesian beauty contests when such information is heterogeneously interpreted by investors. In an overlapping generations rational expectations model of capital markets, we show that, if transparency and clarity of a public disclosure are of comparable magnitude, clarity is of first-order importance in improving price efficiency. Moreover, marginally improving transparency can be detrimental to price efficiency if the initial level of transparency is already sufficiently high.

Information Coefficient: From a New Perspective
Chuan, Yijian,Wu, Lan
SSRN
IC (Information Coefficient) is a widely and deeply accepted measure in active portfolio management.This paper investigates its probability properties that requires basic and in-depth research. First, this paper brings a new perspective on IC: a linear operator of a random unit vector generated by projecting returns onto a unit sphere. The new definition distinguishes IC from correlation coefficients or common statistics like t-statistics. Second, we theoretically solve the maximization of IC in expectation, and specify its almost closed-form solution with directional statistics. Third, simulation analysis reveals the influence of market condition like the stock number on IC, and emphasizes the difference between the optimal solutions and the population mean of returns. Last, empirical studies on Chinese stock market present a set of facts emerging from the unit transformed vectors of cross-sectional returns, and obtain the time series of IC in the real market with rolling windows. Our research profoundly reveals the nature of IC and sharply deepens the understanding of active portfolio management. Both the methodology and empirical study on IC suggest great potential in its further research.

Information Uncertainty and Implied Volatilities
Hong, Jong-Woon,Park, Hyoung-Jin
SSRN
In the U.S. stock and options markets from January 1996 to December 2013, we examine whether information uncertainty explains the discrepancy between historical and implied volatilities in Goyal and Saretto (2009). In addition, we clarified the impact of the uncertainty on the stock market as well as on the options market. In particular, we calculated the performance of our zero-investment option portfolio selling option straddle positions of stocks in the first decile with the lowest discrepancy between the two volatilities and purchasing option straddle positions in the last decile with the highest discrepancy. Moreover, we estimate the returns of these portfolios held by until to the earnings announcement days as well as the returns of the portfolios held by one month. In our results, changes in information uncertainty are in tandem with changes in implied volatility and reduce the predictability of implied volatility for the future realized volatility. Additionally, we show an insignificant change in volatility skew during the time of a significant change in volatility implied from ATM options. Conclusively, we provide novel evidence that the uncertainty of information concerning a firmâ€™s fundamental underlying volatility proposed in Hirshleifer (2001) significantly affects implied volatility.

Integrated Project Delivery Using System Dynamics
Susanto, Eddy,Sihombing, Lukas B.
SSRN
The construction industry is a growth engine to improve economic growth, but most project construction delivery is usually in a linear process. Each process depends on previous work because of their interdependence. This paper strives to compare traditional project delivery and integrated project delivery using a system dynamics method based on a design-bid-build process for construction. The results of this research endeavor to prove that integrated project delivery is more effective than traditional project delivery.

Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data
Célestin Coquidé,José Lages,Dima L. Shepelyansky
arXiv

We apply the recently developed reduced Google matrix algorithm for the analysis of the OECD-WTO world network of economic activities. This approach allows to determine interdependences and interactions of economy sectors of several countries, including China, Russia and USA, properly taking into account the influence of all other world countries and their economic activities. Within this analysis we also obtain the sensitivity of economy sectors and EU countries to petroleum activity sector. We show that this approach takes into account multiplicity of network links with economy interactions between countries and activity sectors thus providing more rich information compared to the usual export-import analysis.

Intermediary Trading and Risk Constraints
Anderson, Christopher,Liu, Weiling
SSRN
Limits to the risk-taking activities of financial intermediaries are important for understanding market stability as well as asset prices, yet they remain difficult to pin down. We propose a novel measure of intermediary risk constraints called the interdealer broker (IDB) index, which captures the portion of total trade volume conducted between dealers using an IDB. Theoretically, when aggregate risk constraints tighten, dealers will use IDBs more in order to redistribute idiosyncratic risk. Empirically, we test our measure in the U.S. Treasury market, where we find that the IDB index has a 0.72 correlation with interest rate risk, as proxied by Value-at-Risk. Furthermore, a one standard deviation increase in the IDB index forecasts a 1.8 percentage point higher annual excess return on a five-year bond. This return predictability holds across different fixed income classes, over varying maturities, as well as out-of-sample.

LIBOR Fallback: A Quantitative Perspective (Quant Summit Presentation Slides)
Henrard, Marc P. A.
SSRN
Slides from the presentation at the Quant Summit 2019.Agenda:- The current status of the fallback improvements- Potential difficulties with the proposed compounding in arrears option- Value transfer in the fallback- The RFR term rates

Long-Term Asset Allocation, Risk Tolerance and Market Sentiment
Erdemlioglu, Deniz,Joliet, Robert
SSRN
This paper studies optimal equity portfolios with long-term horizon under heterogeneous risk aversion levels. We focus on European stocks and empirically show that contemporaneous excess returns of semi-active strategies are negatively associated with market conditions and sentiment. Consistent with our long-horizon perspective, we find that the effects of sentiment measures on semi-active portfolio returns are sizeable and economically relevant, particularly in bull (post-crisis) periods, even after controlling for the five Fama-French factors, momentum, macro indicators and political uncertainty shocks either globally or country-wise. By contrast, the effects of sentiment measures on the passive (benchmark) portfolio are negligible. The results further indicate that realized portfolio returns generated from our long-term strategies are considerably resilient to the episodes of flight-to-safety (risk-off) regimes.

Monetary Policy Transmission to Consumer Financial Stress and Durable Consumption
Georgarakos, Dimitris,Tatsiramos, Konstantinos
SSRN
We examine the effects of monetary policy on household self-assessed financial stress and durable consumption using panel data from eighteen annual waves of the British Household Panel Survey. For identification, we exploit random variation in household exposure to interest rates generated by the random timing of household interview dates with respect to policy rate changes. After accounting for household and month-year-of-interview fixed effects, we uncover significant heterogeneities in the way monetary policy affects household groups that differ in housing and saving status. In particular, an increase in the interest rate induces financial stress among mortgagors and renters, while it lessens financial stress of savers. We find symmetric effects on durable consumption, mainly driven by mortgagors with high debt burden or limited access to liquidity and younger renters who are prospective home buyers.

NGO-GM: Natural Gradient Optimization for Graphical Models
Benhamou, Eric,Atif, Jamal,Laraki, Rida,Saltiel, David
SSRN
This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the celebrated EM approach for learning in graphical models. Actually, our natural gradient based strategy leads to learning optimal parameters for the final objective function without artificially trying to fit a distribution that may not correspond to the real one. We support our theoretical findings with the question of trend detection in financial markets and show that the learned model performs better than traditional practitioner methods and is less prone to overfitting.

Off-chain Bitcoin Trading Puzzle
Aloosh, Arash
SSRN
The immutability of blockchain technology is fundamental to prevent double spending in the Bitcoin system. This article documents puzzling off-blockchain (off-chain) trading volume on Bitcoin exchange platforms. I estimated the volume of off-chain Bitcoin transactions using constraint OLS regressions. I found that around 98% of Bitcoin transactions on Chinese platforms were off-chain. More importantly, the estimated proportion of off-chain transactions in non-Chinese platform volume has dramatically increased from 26% on average before January 2017, to around 76% after January 2017. The limited block size capacity explains only a small fraction of the off-chain Bitcoin trading volume on non-Chinese platforms.

Portfolio diversification based on ratios of risk measures
Mathias Barkhagen,Brian Fleming,Sergio Garcia Quiles,Jacek Gondzio,Jens Kroeske,Sotirios Sabanis,Arne Staal
arXiv

A new framework for portfolio diversification is introduced which goes beyond the classical mean-variance theory and other known portfolio allocation strategies such as risk parity. It is based on a novel concept called portfolio dimensionality and ultimately relies on the minimization of ratios of convex functions. The latter arises naturally due to our requirements that diversification measures should be leverage invariant and related to the tail properties of the distribution of portfolio returns. This paper introduces this new framework and its relationship to standardized higher order moments of portfolio returns. Moreover, it addresses the main drawbacks of standard diversification methodologies which are based primarily on estimates of covariance matrices. Maximizing portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex with potentially multiple local optima. Two complementary global optimization algorithms are thus presented. For problems of moderate size, a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given. We demonstrate through numerical experiments that the introduced diversification measures possess desired properties as introduced in the portfolio diversification literature.

Public-Private-People Partnership as a New Financing Model for Infrastructure Development: A Conceptual Framework
Sihombing, Lukas B.,Santos, Ahmad,Wibowo, Andreas
SSRN
One of the financing schemes in providing an infrastructure project is a public-private-partnership (PPP). PPP is an implementation option and infrastructure service that provides incremental benefits as well as private financing access to promote the government. This study aims to provide a new concept that combines PPP with society (people) to become a public-private-people partnership (PPPP). Therefore, to analyse the state-of-the-art schemes of PPPP, a meta-analysis method is employed that is based on a literature study. However, this PPPP concept is a framework to encourage the society (people) to participate more, especially in land acquisition. The land can be owned by individuals or traditional (communal) society (masyarakat adat). The results of this paper are in the form of a conceptual framework of PPPP as a new financing model for infrastructure development, where the PPPP concept involves the current PPP financing scheme and societal participation, whether in terms of legal aspects and policies or institutional matters. This new concept can help the society (people) to have prosperous welfare together with the institution under PPPP, as well as to not depend on the state budget, as it increases private equity.

Rainy Day Liquidity
Huang, Jingzhi,Li, Xin,SaÄŸlam, Mehmet,Yu, Tong
SSRN
As the largest stakeholder in the corporate bond market, insurance firms may act as rainy day liquidity providers in times of market stress since their cash flows are largely independent of capital market conditions. We theoretically model and empirically support this distinct role played by insurers. Specifically, insurer corporate bond purchases improve bond liquidity while their bond sales do not. Separating the sample into crisis and non-crisis periods and bond groups based on rating and liquidity, we find liquidity provision by insurers to be stronger under stressful conditions. Our empirical analyses reveal that cash flow position and investment horizons strongly influence insurers' purchase of low-rating bonds, and that insurers increase their purchase of low-rating bonds in the financial crisis and after the adoption of the Dodd-Frank Act.

Robust Inference for Consumption-Based Asset Pricing
Kleibergen, Frank R.,Zhan, Zhaoguo
SSRN
The reliability of traditional asset pricing tests depends on: (1) correlations between asset returns and factors; (2) the time-series sample size T compared to the number of assets N. For macro-risk factors, like consumption growth, (1)-(2) are often such that traditional tests cannot be trusted. We extend the Gibbons-Ross-Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, yet they show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.

Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists
Jozef Barunik,Cathy Yi-Hsuan Chen,Jan Vecer
arXiv

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.

Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
Rongju Zhang,Nicolas Langrené,Yu Tian,Zili Zhu,Fima Klebaner,Kais Hamza
arXiv

In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investment goal. This strategy favorably shapes the entire probability distribution of returns, as it simultaneously seeks a desired expected return, cuts off downside risk and implicitly caps volatility and higher moments. To illustrate the effectiveness of this investment strategy, we study a multiperiod portfolio optimization problem with transaction costs and develop a two-stage regression approach that improves the classical least squares Monte Carlo (LSMC) algorithm when dealing with difficult payoffs, such as highly concave, abruptly changing or discontinuous functions. Our numerical results show substantial improvements over the classical LSMC algorithm for both the constant relative risk-aversion (CRRA) utility approach and the proposed skewed target range strategy (STRS). Our numerical results illustrate the ability of the STRS to contain the portfolio value within the targeted range. When compared with the CRRA utility approach, the STRS achieves a similar mean-variance efficient frontier while delivering a better downside risk-return trade-off.

State-of-the-art of Literature Reviews on Project Financing Models for Toll Road Investments
Sihombing, Lukas B.,Latief, Yusuf,Rarasati, Ayomi D.,Wibowo, Andreas
SSRN
The financial sustainability has been a major issue in greenfield toll-road projects. Most of Indonesiaâ€™s 24 toll road concessions already signed by Toll Road Authority, for instance, cannot be put in operation because of financing problems. Uncertainties of long-term project revenues, limitation of budget provided by governments, inadequate government supports for land acquisitions, lack of toll road financing itself and low traffic estimates have been widely cited as potential sources of this unsustainability. Using desk study and meta analysis, this research aims to investigate the state-of-the-art of innovative financing models recently introduced to address financial problems. These models include earmarked tax revenue system, deep discount bonds, take out financing, tax increment financing, land leases, deferred debt, and private donation.

Stochastic Asset Price Dynamics and Volatility Using a Symmetric Supply and Demand Price Equation
Caginalp, Carey,Caginalp, Gunduz
SSRN
A symmetric supply/demand model of price dynamics is developed and used to understand the relationship between price change and volatility. This diï¬€ers from the classical approach in which the expected rate of price change and variance are assumed to be independent. The microeconomic and stochastic analysis leads to the conclusion that a particular measure of the marginal volatility has a minimum shortly before the expected log-price has an extremum. The maxi-mum of the volatility occurs when prices are likely to change most rapidly, and the supply/demand imbalance is greatest. The great bubble and collapse of Bitcoinâ€™s price serves as a test of this analysis. The volatility reached a mini-mum shortly prior to the peak of Bitcoinâ€™s price in December 2018. The model is further developed under the assumption that supply and demand depend on the fundamental value of the asset. Thus the paper is a key step in understand-ing the issue of whether volatility peaks can forecast trading price tops and bottoms. The methodology can be extended beyond log-normal returns and is further compared with an empirical study fo 40 sharp market boom/bust events studied by Sornette et. al. (2017).

The (Un?)Enforceability of Investor Rights in Indian Private Equity
Majumdar, Arjya B.
SSRN
While Private Equity (â€œPEâ€) funding is a preferred vehicle for corporate growth in India, due to the ubiquitous role played by company promoters, extant laws, and a complex regulatory and compliance environment, PE funds prefer to take up a minority shareholding in Indian companies. As a result, PE funds invest in Indian companies in exchange for participation in the companyâ€™s profits either through equity or convertible preferred stock or convertible debt. The PE fund typically also requires a number of investor control rights negotiated as part of the investment, keeping in mind concerns related to minority shareholding in India. While these contractual rights typically do not interfere with the day-to-day management of the company, they serve as a check and balance against promoter opportunism. These rights include provisioning for the investor to participate in the governance of the company through board nomination, quorum requirements and veto powers. Investors may also require downside protection in the form of anti-dilution and pre-emptive exit rights and preferred payments upon liquidation. However, the nature of these investor control rights are departures from the default provisions under Indian company law. These rights, which are borne out of a contractual arrangement between the investor and the company/ promoters, are also subject to Indian contract law under which, contracts in variation of applicable law are void. Additionally, due to excessive delays in the Indian judiciary, any disputes that may arise are not referred to the courts, but are privately arbitrated or settled. Consequently, the enforceability of these contracted rights have never been tested in court. This paper seeks to qualitatively identify the investor control rights typically negotiated by PE funds using a sample of 158 privately held Indian companies which have received investments from non-Indian PE funds in the last five years. This paper will go on to analyse the limitations that Indian corporate and contract law place upon partiesâ€™ freedom to contract, thus raising the question as to whether the rights negotiated by PE investors are enforceable at all. It is hypothesized that some of these rights may not be enforceable in their customary form. This is a draft paper scheduled for publication with the University of Pennsylvania Journal of International Law. This paper would not have been possible without the funding support received from the JGU Research Grants Committee (Grant No JGU/RGP/2018/013). The author is also immensely grateful to Ms Chinar Gupta, Ms Ishita Malhotra, Mr Soumil Desai, Mr Nikhil Kapoor and Mr Dhananjay Salkar, all students of the JGLS Class of 2019, for their research assistance.

The Attitude of IPO Decision in Emerging Markets: Evidence from Egypt
Wagdi, Osama
SSRN
The study has a proposed framework for analysis IPO decision. The study makes a classification of the determinants of IPO decision for two groups. First: The qualitative determinants, but the second is quantitative determinants, the study aims to analyze the first group only; so, the study let Founders, investor & manager assess the attitude for the qualitative determinants of IPO Decision. These findings have important implications for future regulatory measures for stock markets, central banks & taxes agencies.in addition, explaining another side of the agency problem. This study proposes to illustrate the importance of supporting the efforts of the stock exchanges and government agencies to raise awareness among for Founders, investor & manager of the advantages of the public offering. In addition, the key attitude of IPO decision illustrates another side from agency problem.

The Determinants of Corporate Capital Structure: Do CEOâ€™s Cultural Values Affect the Firm Leverage Decision?
Chandrasena, Supun
SSRN
We explore the role of national culture of a Chief Executive Officer (CEO) in firmsâ€™ financing decision, enacted via the tripartite interaction between values, attitudes and behavior. Values are the cultural core that determines attitudes, ultimately steering the behavior of CEOs. Our research is novel as we go beyond the previous focus on firm nationality and examine the influence of cultural values of the individual CEO in the decision making process. By studying CEO and firm level data from 365 major companies in the USA from 2000 to 2015, we find that the CEOsâ€™ cultural values of mastery and embeddedness, have a significant positive impact on firm incremental debt. Our results remain robust to alternative specifications and endogeneity concerns. Using a quantile panel regression approach we allow for the asymmetries between cultural values and leverage and explore the heterogeneous influence cultural values on the leverage decision. We find that the role of cultural values on borrowing is more economically significant at the two extreme levels of the gearing spectrum. Finally using a sample of Non US CEOâ€™s we also show that cultural values are portable and contextual We conclude that cultural values contribute to the behavioral biases of CEOs which could lead to sub-optimal leverage decisions.

The Effect of Political Frictions on Long Term Care Insurance
Liu, Weiling,Liu, Jessica
SSRN
Despite sharply rising prices, the number of companies choosing to operate in the private long term care insurance (LTCI) has dropped from over 100 to just over 30 today. This paper analyzes how product mispricing and regulators' stringency jointly affected insurer dropout in the LTCI market. Using detailed data on LTCI pricing, we show that regulators' political climate â€" including their election cycles, political capital, political affiliation, and campaign funding â€" significantly affected price changes in the LTCI market and, subsequently, insurer profits. We then develop a dynamic structural model of insurance company and regulator interactions. Our model demonstrates how insurer supply and social welfare may be decreasing in regulator stringency when cost shocks are large and unpredictable. Using the calibrated model, we find that removing regulators' election cycles would also significantly increase social welfare â€" equivalent to removing 8% of total cost shocks observed in the LTCI market.

The Financial Model of Land Lease Accelerating Toll Road Land Acquisition â€" A Critical Success Evaluation
Latief, Yusuf,Sihombing, Lukas B.,Rarasati, Ayomi D.,Wibowo, Andreas
SSRN
One of the largest impediments to toll road development is land acquisition. In an ideal perspective, the required land should be acquired before a toll road project begins. However, the reality often does not meet the expectation. The government, which should accept the liability in the first place, often finds it difficult to afford it. Given the stringent budget constraints of the government, project sponsors, for many reasons, should not be expected to take on the full responsibility of dealing with it either. Innovative instruments other than that practiced now must be fostered. In this paper, a discourse on the application of a land lease model is provided. This has been widely used in many other countries, and a set of success factors has been identified to make this model work in Indonesia. A total of 23 critical success factors (CSFs) have been identified from a literature review. A Delphi survey involving a panel of knowledgeable experts was employed to examine toll road authority, guarantee institution, and private investor group variables. Based on the survey, it has been found that the quality of land lease data, the design plan and integrated location, the transparent negotiation on an investment agreement, as well as agreements negotiated to protect private investors and the government were the most important CSFs.

The Impact of Policy Elements on the Financing Costs of RE Investment â€" The Case of Wind Power in Germany
Olaf, Giebel,Breitschopf, Barbara
SSRN
Renewable energy support mechanisms affect the attractiveness of projects by influencing uncertainties in revenues or expenditures and ultimately result in a change in the financing costs. The influence of feed-in tariffs on financing costs was investigated. 26 wind onshore investors were surveyed in a conjoint Analysis and the results were used in a cash flow model to quantify the impact. The introduction of premium models under a fixed remuneration tariff scheme seems to increase the financing costs considerably.

The New Toll Road Financing Model Determination
Rarasati, Ayomi,Sihombing, Lukas B.,Wibowo, Andreas,Latief, Yusuf
SSRN
There are three main problems in Indonesia toll road financing model. First, it is the lack of state budget and delayed funding distribution for land acquisition. The second problem is the lack of financial ability of the winning company which leads to financial closing failure. The third problem is related to the land acquisition process that a special purpose company has to obtain for 100% before it can propose a loan from a bank. Therefore, it is essential to develop a new financial modeling to be implemented in Indonesian toll road development. Seven financial models were identified as having been implemented successfully and effectively. These models are earmarked tax revenue, deep discount bonds, take out financing, tax increment financing, land leases, deferred debt, and private donation. However, these models have never been utilized for toll road projects in Indonesia. Therefore, this research aims to explore those seven financial models and determine the most suitable one to be implemented in Indonesia. Literature review and in-depth interview with the key stakeholders were conducted to collect the data. The results subsequently show that there are four models that have never been implemented in Indonesia. However, the combination of deep discount bond and land leases is the most suitable financing model for Indonesian toll road industry.

The Role of Credit Rating Agencies in Addressing Gaps in Micro and Small Enterprise Financing: The Case of India
Shankar, Savita
SSRN
We describe the common financing challenges faced by micro, small, and medium-sized enterprises (MSMEs) in India and some important measures taken to address them, with a focus on the credit rating scheme implemented in 2000. We examine the usefulness as well as the limitations of the scheme, drawing on interviews with rating agencies and MSMEs. With credit rating being an expensive exercise, the availability of government subsidies under the scheme has been an important factor in encouraging MSMEs to get themselves rated, thereby reducing information asymmetry with banks and enabling access to credit. Given the large number of unbanked MSMEs in the country, leveraging the data generated by MSME lending and credit rating in the country through the creation of a credit risk database is necessary. Lenders will then be able to tap into the collective data generated to make more informed credit decisions with regard to MSMEs without relying on subsidies.Over 63 million micro, small, and medium-sized enterprises in India generate lending and credit rating data. How can lenders leverage these to make informed credit decisions?

The fractional and mixed-fractional CEV model
Axel A. Araneda
arXiv

The continuous observation of the financial markets has identified some stylized facts which challenge the conventional assumptions, promoting the born of new approaches. On the one hand, the long-range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by stationary independent increments, by a fractional version. On the other hand, the CEV model addresses the Leverage effect and smile-skew phenomena, efficiently. In this paper, these two insights are merging and both the fractional and mixed-fractional extensions for the CEV model, are developed. Using the fractional versions of both the Ito's calculus and the Fokker-Planck equation, the transition probability density function of the asset price is obtained as the solution of a non-stationary Feller process with time-varying coefficients, getting an analytical valuation formula for a European Call option. Besides, the Greeks are computed and compared with the standard case.

Tobinâ€™s Q of Honda Motor Company, Limited and its Determinants from 2013 to 2017
Khoo, Shi Shean
SSRN
A firmâ€™s market capitalization can be influenced by internal or external factors. This may be caused by and linked to corporate governance failures and the changes of macroeconomic factors. This paper attempted to investigate the internal determinants (corporate governance index, return on assets, return on equity, Altman Z) and external determinants (gross domestic product, unemployment rates and exchange rate) of Tobinâ€™s Q and how they influence Tobinâ€™s Q of Honda Motor Company, Limited from 2013 to 2017. The importance of corporate governance will also be delivered indirectly in this study. Ordinary Least Square analysis (OLS) was used to study the significance of independent variables towards Tobinâ€™s Q. The findings showed that Altman Z (internal determinant) was positively significant to the Tobinâ€™s Q ratio and influenced Tobinâ€™s Q the most. This study also suggested the firm to focus on its corporate governance principle, which is transparency to avoid bankruptcy.

UN PRI Signatories' Ethics: Walk the Talk or Talk the Talk? Serious Ethical First Movers vs. Late Free Riders
Bauckloh, Tobias,Utz, Sebastian,Zeile, Sebastian,Zwergel, Bernhard
SSRN
The UN Principles for Responsible Investment (UN PRI) is a prominent global initiative by institutional investors to support the development of a sustainable financial system. Neither the participation in this initiative nor the implementation of the principles by its signatories are compulsory. In our paper, we investigate to what extent UN PRI signatories base their business activities on ethics. With an event study design, we show causal evidence that UN PRI signatories perform better in the ethical dimension than matched non-signatories after the signature date. Early signatories commit themselves significantly stronger towards ethical principles than later signatories.

University rankings from the revealed preferences of the applicants
László Csató,Csaba Tóth
arXiv

A methodology is presented to rank universities on the basis of the lists of programmes the students applied for. We exploit a crucial feature of the centralised assignment system to higher education in Hungary: a student is admitted to the first programme where the score limit is achieved. This makes it possible to derive a partial preference order of each applicant. Our approach integrates the information from all students participating in the system, is free of multicollinearity among the indicators, and contains few ad hoc parameters. The procedure is implemented to rank faculties in the Hungarian higher education between 2001 and 2016. We demonstrate that the ranking given by the least squares method has favourable theoretical properties, is robust with respect to the aggregation of preferences, and performs well in practice. The suggested ranking is worth considering as a reasonable alternative to the standard composite indices.