Research articles for the 2019-06-30
SSRN
In this paper, we provide an alternative framework for constructing an arbitrage-free European-style option surface. The main motivation for our work is that such a construction has rarely been achieved in the literature so far. The novelty of our approach is that we perform the calibration and interpolation in the put option space. To demonstrate the applicability of our technique, we extract the model-free implied volatility from S&P 500 index options. Subsequently, we compare its information content to that of the CBOE VIX index. Our empirical tests indicate that information content of the option-implied volatility values based on our method are superior to the VIX index.
arXiv
Possibilistic risk theory starts from the hypothesis that risk is modelled by fuzzy numbers. In particular, in a possibilistic portfolio choice problem, the return of a risky asset will be a fuzzy number. The expected utility operators have been introduced in a previous paper to build an abstract theory of possibilistic risk aversion. To each expected utility operator one can associate a notion of possibilistic expected utility. Using this notion, we will formulate in this very general context a possibilistic choice problem. The main results of the paper are two approximate calculation formulas for corresponding optimization problem. The first formula approximates the optimal allocation with respect to risk aversion and investor's prudence, as well as the first three possibilistic moments. Besides these parameters, in the second formula the temperance index of the utility function and the fourth possibilistic moment appear.
arXiv
Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryptocurrency trade. We find that when engaging in cryptocurrency trading investors simultaneously increase their risk-seeking behavior in stock trading as they increase their trading intensity and use of leverage. The increase in risk-seeking in stocks is particularly pronounced when volatility in cryptocurrency returns is low, suggesting that their overall behavior is driven by excitement-seeking.
SSRN
If bail-in is credible, risk premia on bank securities should decrease as funding sources junior to and alongside them in the creditor hierarchy increase. Other things equal, we find that when banks have more equity and less subordinated debt they have lower risk premia on both. When banks have more subordinated and less senior unsecured debt, senior unsecured risk premia are lower. For percentage point changes to an average balance sheet, these reductions would offset about two thirds of the higher cost of equity relative to subordinated debt and one third of the spread between subordinated and senior unsecured debt.
SSRN
We develop a new moment-ratio test, which extends the variance-ratio test to higher co-moments, i.e., skewness and kurtosis. The test establishes a significant intertemporal dependency in all higher moments of equity returns, which is strong enough to counteract the central limit theorem. The intertemporal dependency is both horizon and portfolio-specific. Hence, we cannot make a general statement about the long-run risk without making some strong assumptions about the intertemporal dependency in returns. Consequently, the common practice of extrapolating the short-run risk by assuming independent returns will severely bias the expected long-run risk.
SSRN
The internal audit function (IAF) has become one of the main pillars of good corporate governance in recent years. Empirical findings show that the size of the IAF varies considerably across companies. This study analyzes the effect of selected company characteristics on the size of the IAF. First, a theoretical model outlines the idea that IAF size depends on company characteristics that affect existing information asymmetries within the company. Second, we test this relationship by analyzing comprehensive survey data obtained from chief audit executives from 283 Austrian, German, and Swiss organizations. Using a nonparametric regression approach, we identify a set of company characteristics as key drivers of IAF size. Furthermore, the empirical analysis identifies threshold levels for several metric company characteristics, such as the number of employees and the number of subsidiaries, whose effects on the size of the IAF change its intensity. Additionally, our study provides a helpful benchmark for the appropriateness of a company’s IAF size, which is of value for practitioners.
SSRN
I document substantial effects on financing costs and debt contracting behavior following data breaches of public firms. Using data breach events of publicly listed companies during the period between 2005 and 2015, I find that lenders charge breached firms with 15 to 20 percent larger spreads, and tighten covenant intensity, consistent with a shift in control rights over cash flows. The effect is more pronounced for breaches of financial information rather than of employee or non-financial information, for Fortune 500, dividend-paying, capital intensive firms, but that lack cyber-risk management. Consistent with creditors amending debt contracts after a reassessment of the risk-profile of healthy firms following negative shocks, firms' cash flows become more volatile, profitability drops, and the likelihood of a second breach increases. Moreover, my results are not driven by full ex-ante mispricing by banks.
arXiv
The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior distribution of parameters and latent variables which can then be used for predicting future volatilities. The package can straightforwardly be employed as a stand-alone tool; moreover, it allows for easy incorporation into other MCMC samplers. The main focus of this paper is to show the functionality of stochvol. In addition, it provides a brief mathematical description of the model, an overview of the sampling schemes used, and several illustrative examples using exchange rate data.
SSRN
We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short, medium and long term effects by looking at the Sharpe ratio (SR). We calculate the Sharpe ratio of our trading rule as a function of the probability distribution function of the underlying traded asset and compare it with data. We show that if the performance is mainly due to presence of autocorrelation in the returns of the traded assets, the SR as a function of the portfolio formation period (look-back) is very different from performance due to the drift (average return). The SR shows that for look-back periods of a few months the investor is more likely to tap into autocorrelation. However, for look-back larger than few months, the drift of the asset becomes progressively more important. Finally, our empirical work reports a new long-term effect, namely oscillation of the SR and propose a non-stationary model to account for such oscillations.
SSRN
This paper investigates the performance of various conditional volatility models to forecast the second moment of tanker freight rates. Justified by existing theoretical and empirical evidence, we focus on asymmetric Markov regime-switching models to study the major global routes for long-haul trade of crude oil during the sample period from June 2000 to May 2015. Moreover, in contrast to a number of existing studies, we examine seasonally adjusted freight rates. We find that regime-switching GARCH models outperform their single-regime complements in terms of in-sample fit and out-of-sample forecasting accuracy. In particular, the asymmetric MRS-EGARCH and MRS-APARCH exhibit superior in- and out-of-sample performance. To additionally examine the applicability in freight risk management, we compare Value-at-Risk and Expected Shortfall forecasts. Our results show that accounting for volatility regimes and asymmetry does not enhance the performance of one-day-ahead forecasts of either risk measure for both long and short trading positions.
SSRN
We investigate whether more salient fee disclosure mitigates bond market professionals' ability to charge retail investors high fees. We explore changes in fees around FINRA's 2018 amendment of the customer confirmation rule, requiring corporate bond market professionals to explicitly disclose the fee (markup) on some retail trades. Investors could have inferred the fee before the rule change using historical transaction prices. Nonetheless, we find that fees associated with trades subject to explicit fee disclosure decline after the rule change, relative to trades that are not subject to explicit fee disclosure. Our findings are pronounced among bonds for which fees were highest before the rule change. In sum, our evidence shows that the nature of fee disclosure (i.e., explicit or implicit) has real effects on corporate bond market professionals' ability to charge high fees for their services.
SSRN
In this study, we investigate how financial analysts actually implement the Sum-of-the-Parts (SOTP) valuation framework. Although SOTP constitutes a popular valuation approach among sophisticated practitioners and investors, it is mostly ignored by researchers and academics. We adopt a structured content analysis of 265 equity research reports written by 33 investment brokerage houses for 140 UK-based firms. We find that analysts typically use EBITDA multiples to implement SOTP. We also show that analysts usually identify more segments in their SOTP analysis compared to the reportable segments in the firms’ annual reports based on IFRS 8. Furthermore, financial analysts are more likely to consider SOTP the dominant or preferred valuation model in their report. Finally, although SOTP seems theoretically ideal to estimate the value of a multi-segment firm, we do not find empirical evidence to support the hypothesis that SOTP significantly outperforms a full-blown Discounted Cash Flow (DCF) model, when the latter is used separately to value the company as a whole.
arXiv
We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS). Our solution is important because each method offers strengths and weaknesses that expose or conceal flaws in the subject strategy. A key weakness of Market Replay is that the simulated market does not substantially adapt to or respond to the presence of the experimental strategy. IABS methods provide an artificial market for the experimental strategy using a population of background trading agents. Because the background agents attend to market conditions and current price as part of their strategy, the overall market is responsive to the presence of the experimental strategy. Even so, IABS methods have their own weaknesses, primarily that it is unclear if the market environment they provide is realistic. We describe our approach in detail, and illustrate its use in an example application: The evaluation of market impact for various size orders.
SSRN
The aim of this paper is to analyse the link between bank income smoothing and shareholder structure, using a sample of Central European banks. Using data for 2004-2014, we demonstrate that foreign banks incite their subsidiaries to use loan loss provisions for income smoothing purposes. This process intensifies after the outbreak of the financial crisis and persists after the crisis. State banks show varying degrees of income smoothing, with more intense smoothing before the crisis and a diminished link between provisions and income during- and after the crisis. Overall, we provide important evidence for an effect of foreign bank ownership upon loan loss reserve policy in subsidiary banks, extending the existing evidence on shock transmission from home to host countries only through the credit supply channel.
arXiv
Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial and software that allows to easily fit SV models to data is rare. We aim to alleviate this issue by presenting novel implementations of four SV models delivered in two R packages. Several unique features are included and documented. As opposed to previous versions, stochvol is now capable of handling linear mean models, heavy-tailed SV, and SV with leverage. Moreover, we newly introduce factorstochvol which caters for multivariate SV. Both packages offer a user-friendly interface through the conventional R generics and a range of tailor-made methods. Computational efficiency is achieved via interfacing R to C++ and doing the heavy work in the latter. In the paper at hand, we provide a detailed discussion on Bayesian SV estimation and showcase the use of the new software through various examples.
arXiv
We develop a dual control method for approximating investment strategies in incomplete environments that emerge from the presence of market frictions. Convex duality enables the approximate technology to generate lower and upper bounds on the optimal value function. The mechanism rests on closed-form expressions pertaining to the portfolio composition, whence we are able to derive the near-optimal asset allocation explicitly. In a real financial market, we illustrate the accuracy of our approximate method on a dual CRRA utility function that characterizes the preferences of some finite-horizon investor. Negligible duality gaps and insignificant annual welfare losses substantiate accuracy of the technique.
SSRN
We develop a dual control method for approximating investment strategies in incomplete environments that emerge from the presence of market frictions. Convex duality enables the approximate technology to generate lower and upper bounds on the optimal value function. The mechanism rests on closed-form expressions pertaining to the portfolio composition, whence we are able to derive the near-optimal asset allocation explicitly. In a real financial market, we illustrate the accuracy of our approximate method on a dual CRRA utility function that characterizes the preferences of some finite-horizon investor. Negligible duality gaps and insignificant annual welfare losses substantiate accuracy of the technique.
SSRN
Shocks that hit part of the financial system, such as the subprime mortgage market in 2007, can propagate through a complex network of interconnections among financial and non-financial institutions. As the financial crisis of 2007-2009 has shown, the consequences for the entire economy of such systemic risk materializing can be catastrophic. Following the crisis, economists and policymakers have become increasingly aware that the structure of the financial system is a key determinant of systemic risk. A wide consensus now exists among them that network theory is the natural framework for studying systemic risk. Yet, most of the existing rules in financial regulation are still “atomistic,†in that they fail to incorporate the fact that each individual institution is part of a wider network. This article shows that policies building upon insights from network theory (network-sensitive policies) can address systemic risk more effectively than traditional atomistic policies, also in areas where an atomistic approach would seem natural, such as the corporate governance of systemically important financial institutions. In particular, we consider four prescriptions for the governance of systemically important institutions (one on directors’ liability, two on executive compensation and one on failing financial institutions’ shareholders appraisal rights in mergers) and show how making them network-sensitive would both increase their effectiveness in taming systemic risk and better calibrate their impact on individual institutions.
arXiv
This paper solves the optimal investment and consumption strategies for a risk-averse and ambiguity-averse agent in an incomplete financial market with model uncertainty. The market incompleteness arises from investment constraints of the agent, while the model uncertainty stems from drift and volatility processes for risky stocks in the financial market. The agent seeks her best and robust strategies via optimizing her robust forward investment and consumption preferences. Her robust forward preferences and the associated optimal strategies are represented by solutions of ordinary differential equations, when there are both drift and volatility uncertainties, and infinite horizon backward stochastic differential equations, coupled with ordinary differential equations, when there is only drift uncertainty.
SSRN
We examine how heterogeneity in organizational structure affects private firm earnings quality in the European Union. Organizational structure refers to whether the firm is organized as a single legal entity (standalone) or as a business group. Private firms can be organized either way, while public firms are de facto groups. Even though private firms are not affected by market forces, we show that private business groups face greater stakeholder pressure for earnings quality than do standalone firms, while standalone firms have stronger tax minimization incentives. Due to these differences in nonmarket forces, private business groups have higher earnings quality than standalone firms. This heterogeneity among private firms is an important unexplored factor in the study of private firms, affecting the comparison between public and private firm earnings quality. We find that overall, public firms have higher earnings quality than private firms but this relation reverses when we control for nonmarket forces by examining business groups only.
SSRN
The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk- constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g. less risk averse investors lose more portfolio value in the financial crises but outperform their more risk averse counterparts in bull markets.
arXiv
An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms which are well suited to analyze the risk due to events having a spatial extent, precisely such as environmental phenomena. The axiom of asymptotic spatial homogeneity is of particular interest since it allows one to quantify the rate of spatial diversification when the region under consideration becomes large. In this paper, we first investigate the general concepts of spatial risk measures and corresponding axioms further and thoroughly explain the usefulness of this theory for both actuarial science and practice. Second, in the case of a general cost field, we give sufficient conditions such that spatial risk measures associated with expectation, variance, Value-at-Risk as well as expected shortfall and induced by this cost field satisfy the axioms of asymptotic spatial homogeneity of order $0$, $-2$, $-1$ and $-1$, respectively. Last but not least, in the case where the cost field is a function of a max-stable random field, we provide conditions on both the function and the max-stable field ensuring the latter properties. Max-stable random fields are relevant when assessing the risk of extreme events since they appear as a natural extension of multivariate extreme-value theory to the level of random fields. Overall, this paper improves our understanding of spatial risk measures as well as of their properties with respect to the space variable and generalizes many results obtained in Koch (2017).
SSRN
This paper provides evidence of the ability of a cash flow-based life cycle proxy, developed by Dickinson (2011), to explain the propensity of firms to pay dividends, which can vastly improve our understanding of the life cycle effect. Our results show that the propensity to pay manifests a nonlinear relation with the five stages of a firm’s life cycle, and that the commonly used life cycle proxy RE/TE cannot reconcile important features of the data. The cash flow-based proxy also captures theoretically consistent changes in payout policy when a firm transitions from one life cycle stage to another.
SSRN
During the year 2017, hurricanes Irma and Maria wreaked havoc in the Caribbean and severely disrupted entire societies. This study extends the literature on the impact of climate change on Small Island Developing States by investigating the impact of hurricanes and tropical storms on stock and foreign exchange markets in Jamaica. The study finds that in a majority of cases, the passage of hurricanes and tropical storms lead to significant losses on Jamaica’s stock and foreign exchange markets, and in some cases, exceed the widely reported losses from damage to property and infrastructure. The implication is that failure to account for financial market losses can significantly understate the economic and financial impact of natural disasters.The results reinforce the vulnerability of Small Island Developing States like Jamaica to natural disasters and provide another dimension to the scale of the negative impacts of climate change on these nations. The findings also suggest that measures to mitigate the effects of climate change and natural disasters not only minimize loss of life, property and infrastructure, but may also serve to protect the value of pensions and other investments held in the stock market, as well as help maintain the stability of the currency.
SSRN
The recent financial crisis was associated with a large and prolonged deterioration to the collateral value and to the collateral-based credit supply. I calibrate a model to explore the impact of collateral shocks on real firm behavior. I discover that: (i) a negative shock to the collateral value depresses the business activities by tightening the borrowing capacity. Such adverse impact is alleviated (worsened) by a lower (higher) productivity-driven credit demand; (ii) following a negative collateral shock, the reduction of labor adjustment costs causes the firms to decrease their activities to a less extent, and such positive effects of labor adjustment flexibility are more pronounced for firms facing a high level of productivity (demand). Empirically, I find that a lower labor unionization rate can mitigate the negative impact of supply shocks on the high-demand firms during the crisis.
SSRN
This paper investigates the moderating impact of FDI & FPI in the association of macro-economic variables along with Oil prices & Index returns. Monthly data has been used from the period 2005 to 2018. Efficient unit root & break point unit root tests results indicate that all variables are stationary at 1st difference. Co -integration test results signify the presence of long-run relationship in model. GARCH (1,1) model has been applied for analyzing the volatility in the data series. Furthermore, least square method is employed to check dependency & fitness level of model. In order to investigate the moderating impact, regression technique has been applied. Findings of LSM technique indicate that index returns aren’t significantly dependent on macro-economic variables on 1st difference of data series because variables predicting behavior has been changed with respect to stationarity of data. Exchange rate & interest rate have negative significant association with index returns. Oil prices & foreign direct investment have positive relationship with stock market return. FDI & FPI are unable to moderate significantly model dynamics. For estimating the panel regression model, 11 different sectors data is used and results show that exchange rate & oil prices have positive significant impact on sector wise price change but interest rate has significant negative association.
SSRN
Annual electricity consumption for cryptocurrency mining is growing yearly, driven by the increasing difficulty in mining. Total carbon production from mining now likely exceeds that generated by the entire nation of Portugal. This is a feature, not a bug, in cryptocurrencies. This paper investigates how Bitcoin's price volatility and the underlying dynamics of cryptocurrency's mining characteristics affect the energy markets, utilities companies, and green ETFs. The results show that continued cryptocurrency energy-usage impacts the performance of energy sector, which emphasises the importance of further assessment of environmental impacts of cryptocurrency growth.
SSRN
In November 2012, Norway’s sovereign wealth fund (NBIM) unexpectedly announced that it would foster better corporate governance practices in its portfolio firms by articulating specific governance expectations. We use this sudden change in governance preferences as a natural experiment to understand shareholder influence among active ownership investors. We first document how the fund re-balanced its portfolio to achieve this governance objective believed to be aligned with investment objectives. We then show how firms for which the fund is an important investor and also firms that are very important to the fund reacted by aligning their corporate governance following NBIM expectations. Marginal investment changes and governance changes become more correlated in the new equilibrium. We also examine the heterogenous response of firms to institutional pressures. This paper advances existing research on active owners’ influence on firms, and particularly on firm governance practices.
SSRN
We examine disclosures of business outlook by rank-and-file employees on Glassdoor.com. Glassdoor.com is a social media platform where employees can share their views publicly and anonymously. We find that employee disclosures are more highly associated with loan spreads in private lending agreements as those disclosures become more relevant (i.e., when the borrowing firm has higher information opacity, has more dispersed operations, or is more financially constrained) and as those disclosures become more reliable (i.e., as the number of employee ratings increases, when those ratings are based proportionately more on current employees, and for firms that are more labor-intensive.). The results are consistent with employee disclosures providing useful inside information that otherwise may not have been disclosed publicly by upper-level managers. The rapid growth in information disclosed on social media platforms provides researchers an important and interesting setting to explore how alternative sources of information may be generated and disseminated by various stakeholders.
SSRN
We study the ex ante stock market reactions to events leading up to China’s convergence to International Financial Reporting Standards (IFRS). The literature consistently shows that the benefits of mandatory IFRS convergence are concentrated in countries with stronger legal enforcement and investor protection. Given that these institutional characteristics are weaker in China relative to more developed Western economies, whether mandating IFRS will benefit the Chinese capital market is an interesting and important, but unanswered question. We find that the Chinese stock market reacts favorably to events leading up to IFRS convergence, and this effect is more pronounced among firms with greater dependence on external capital. This result suggests the market anticipates that such firms will benefit more from IFRS convergence, possibly because of improved financial reporting quality and access to external financing. Additional tests confirm that the value relevance of accounting numbers for these firms is higher following IFRS convergence.
