Risky Portfolio Research Articles

Bank Regulation and Market Discipline in the Presence of Risk-Shifting Incentives

This paper presents a bank capital structure model in which equity holders can increase asset risk once debt is in place. I study the effects of capital requirements and subsidized deposit insurance on the bank's privately optimal funding and operational risk level. The model predicts that there are synergetic effects of regulation and market discipline. When the regulator sets the capital charge and deposit insurance premium payments sufficiently high for a risky portfolio, the bank commits to the low-risk asset portfolio by setting a lower leverage ratio. This market discipline effect disappears when the regulatory costs become too high.

The Cross-Section of Household Preferences

This paper estimates the cross-sectional distribution of Epstein-Zin preference parameters in a large administrative panel of Swedish households. We consider life-cycle model of saving and portfolio choice that incorporates risky labor income, safe and risky financial assets inside and outside retirement accounts, and real estate. We study middle-aged stock-owning households grouped by education, industry of employment, and birth cohort as well as by their accumulated wealth and risky portfolio shares. We find some heterogeneity in risk aversion (a standard deviation of 0.47 around a mean of 5.24 and median of 5.30) and considerable heterogeneity in the time preference rate (standard deviation 6.0% around a mean of 6.2% and median of 4.1%) and elasticity of intertemporal substitution (standard deviation 0.96 around a mean of 0.99 and median of 0.42). Risk aversion and the EIS are almost cross-sectionally uncorrelated, in contrast with the strong negative correlation that we would find if households had power utility with heterogeneous risk aversion. The TPR is weakly negatively correlated with both the other parameters. We estimate lower risk aversion for households with riskier labor income and higher levels of education, and a higher TPR and lower EIS for households who enter our sample with low initial wealth. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Women as “gold dust”: Gender diversity in top boards and the performance of Italian banks

AbstractThis paper investigates the effects on riskiness and performance of gender diversity in Italian bank boards. Italy deserves special attention because European comparisons for the 2000s show that it was among the European Union countries where women were least represented in bank boardrooms, and this gap remains even in a quota law regime. Our main econometric results suggest that gender diversity may have a positive impact on the credit portfolio riskiness and on profitability. Both results may be driven by women's higher risk aversion and their higher propensity to monitor activities, suggesting that women are “gold dust” for Italian banks. These findings, obtained by using a unique data set over the period 1995–2010 and by taking into account possible sources of endogeneity, support the effectiveness on bank performance of greater female presence on boards. Moreover, our econometric set‐up could offer a benchmark for further analysis on gender diversity effects for many countries, where a gender quota law is still not in place.

Stock Investment Portfolio Analysis with Single Index Model

In order to evaluate an optimal portfolio, an important step that investors or investment managers is portfolio analysis. In stock portfolio analysis, methods that can be used include the Markowitz approach and the Single Index Model. This study aims to apply the Single Index Model in finding the beta value of an efficient portfolio line, so that investors can determine the stocks and the proportion of funds needed to form an optimal portfolio. In this study, the data sources used were 1) market share price index that represents market factor or market data, 2) SBI interest rates that represents risk free (rf) and 3) The share prices of PT Ace Hardware Indonesia Tbk, PT Indocement Tunggal Perkasa Tbk and PT Matahari Putra Prima Tbk. The weight of each share in the active portfolio (Wi0) at Active Pf A 1.0000 is ACES of 0.1729, INTP of 0.0460 and MPPA of 0.7811. Then the alpha of the ACES active portfolio is 0.0051, INTP is 0.0002 and the MPPA is 0.0184. Then the calculation results show the residual variance in the active ACES portfolio is 0.0041, INTP is 0.0001 and MPPA is 0.0147. The variance of the Optimal Risky Portfolio of the variance index portfolio and the residual variance of the active portfolio is 0.1054.

Resilience in the Storm: Adaptive Robo-Advisors Outperform Human Investors during the COVID-19 Financial Market Turmoil

Despite the increasing popularity of robo-advisors (RAs), empirical evidence on their performance, especially during market downturns, is highly limited. We study the impact of using RAs on investment performance during the 2020 financial crisis caused by the COVID-19 global pandemic. We obtain daily portfolio and transaction data of investors on an online investment platform. Besides making investment decisions by themselves, investors can also leverage an RA system offered by the platform. We match RA users with other investors who did not use the RA with similar characteristics before the market crash, then compare their portfolio returns within four weeks after the crash. We find that RA users experienced significantly fewer losses during the market downturn. Moreover, our analyses of trading strategies show that RA users held less risky portfolios, whereas other investors stayed with their status quo and did not reduce the risk of their portfolios, which partly accounted for the performance discrepancy. During the subsequent period of market recovery, we observe that RA users were able to maintain their performance advantage. Overall, by trading adaptively to adjust the portfolio risk levels in response to market movements, the RA system mitigated losses and benefited its users. Further analyses of effect heterogeneity show that younger users or users with less investment experience benefited more from the RA, because they delegated a larger proportion of their assets to be managed by the RA. Our work offers a direct empirical assessment of RAs’ performance during a severe market downturn, and advances the understanding of algorithmic decision-making in the financial markets.

Asset Allocation for “End-State” Portfolios

In recent years many US corporate pension plans have closed and entered their “end-state.” As end-state plans have become more prevalent, their special portfolio management challenges, including asset allocation, have gained attention. Pure immunization with public fixed income assets (“hibernation”) is a possible investment management strategy to try to minimize funding ratio variability. But this may not be sufficient for all end-state plans. For example, mortality risk could cause the actual cash liabilities to deviate from the estimated cash liabilities. This risk may argue for the inclusion of return-seeking assets. In addition, some plans have had good performance experience with their illiquid private assets (e.g., private real estate, private equity, and private credit funds). How can a CIO evaluate the potential of these private assets remaining in their end-state portfolio? We use our asset allocation framework (OASIS™) to help end-state investors solve for optimal asset allocations. An asset allocation solution seeks to maximize the end-state portfolio’s expected horizon value while meeting future cash obligations at a desired confidence level and keeping the funded status at a target level of stability over the investment horizon. We show that private assets can play an important role in helping end-state portfolios achieve their return objectives while meeting their liquidity and funded status stability constraints. CIOs managing end-state portfolios may impose additional constraints to address their specific concerns. For example, CIOs may impose an upper limit on their total allocation to private assets, impose a floor on the plan’s funding ratio, or express views on expected private asset performance and their fund-selection skills. Making asset allocation constraints more restrictive typically implies a less risky portfolio, with a lower allocation to private assets. The OASIS framework helps CIOs measure this tradeoff between performance and their constraints, allowing them to make better business decisions.

Provision of Longevity Insurance Annuities

Longevity insurance annuities are deferred annuities that begin payment at advanced ages, such as age 82. They provide insurance against running out of money in old age. They simplify the retirement spend-down calculations in that they can be used to convert a problem with an unknown end date (date of death) to one with a fixed end date, which is the start of the longevity insurance annuity. They may allow retirees to have riskier portfolios because they are a steady source of income. In this study, we compared the provision of longevity insurance annuities in the private and public sectors. We analyzed the need for longevity insurance annuities using Monte Carlo simulations that demonstrate the risk of running out of money in a 401(k) account and modeled how longevity annuities might work in practice.

Saving Markowitz: A Risk Parity Approach Based on the Cauchy Interlacing Theorem

It is well known that Markowitz Portfolio Optimization often leads to unreasonable and unbalanced portfolios that are optimal in-sample but perform very poorly out-of-sample. There is a strong relationship between these poor returns and the fact that covariance matrices that are used within the Markowitz framework are degenerated and ill-posed, leading to unstable results by inverting them, as a consequence of very small eigenvalues. In this paper we circumvent this problem in two steps: the enhancement of traditional risk parity techniques, which consider only volatility, aiming to avoid matrix inversions (including the widespread Naive Risk Parity model) within the Markowitz framework; the preservation of the correlation structure, as much as possible, aiming to isolate a healthy portion of the correlation matrix, that can be inverted without generating unstable and risky portfolios, aiming to rescue the original Markowitz framework, by means of using the Cauchy Interlacing Theorem. Using Brazilian and US market data, we show that the discussed framework enables one to build portfolios that outperform the traditional and the newest risk parity techniques.

Safety Transformation and the Structure of the Financial System

ABSTRACTThis paper studies how a financial system that is organized to efficiently create safe assets responds to macroeconomic shocks. Financial intermediaries face a cost of bearing risk, so they choose the least risky portfolio that backs their issuance of riskless deposits: a diversified pool of nonfinancial firms' debt. Nonfinancial firms choose their capital structure to exploit the resulting segmentation between debt and equity markets. Increased safe asset demand yields larger and riskier intermediaries and more levered firms. Quantitative easing reduces the size and riskiness of intermediaries and can decrease firm leverage, despite reducing borrowing costs at the zero lower bound.

Corporate tax, financial leverage, and portfolio risk

We examine the theoretical implications of corporate income tax for a risky portfolio in a aggregate-endowment economy. In this model, corporate income tax affects the portfolio risk associated with the rebalancing motive during market clearance. An asset is defined as a portfolio of stocks and bonds whose portfolio weights are similar to financial leverage. Corporate tax can decrease after-tax consumption from dividends (increase leverage) and increase the tax shield that increases dividends (decrease leverage). Changes in dividends are responsible for the correlation between expected dividend growth and consumption growth and, thus, affect stock pricing and returns. Overall, the model is characterized by tax-induced portfolio risk associated with financial leverage.

Countercyclical Income Risk and Portfolio Choices: Evidence from Sweden

Using Swedish administrative panel data, we show that workers facing higher left-tail income risk when equity markets perform poorly are less likely to participate in the stock market and, conditional on participation, have lower equity shares. We call this measure of income risk cyclical skewness'' and show that it is a better predictor of equity holdings than other income risk measures such as variance, covariance, and counter-cyclical volatility. In line with theory, our findings are stronger at the beginning of the life-cycle, are not significant for individuals with substantial financial wealth, and are stronger when we focus on permanent income shocks. Finally, within their risky portfolio, workers put less weight on securities generating negative returns when their own income risk increases.

Comparison of aggregation, minimum and maximum of two risky portfolios with dependent claims

The comparison of two risky portfolios has always been of interest in insurance and finance. Classically, it is often assumed that the portfolio claims are independent, but in practice, this assumption is not usually true and we need to study portfolios with dependent claims. In this paper, we consider two risky portfolios with dependent claims whose dependencies are modeled using Archimedean copulas and compare the aggregation and minimum of these portfolios with respect to the Laplace transform order. Moreover, we compare the maxima of two interdependent portfolios by the usual stochastic order.

Mean-variance dynamic optimality for DC pension schemes

In this paper we deal with the mean-variance portfolio selection for a defined contribution (DC) pension fund. Since this problem is time-inconsistent, a number of papers have proposed to tackle it through either a Nash equilibrium approach or a precommitment strategy. Here, we adopt the dynamically optimal approach introduced by Pedersen and Peskir (Math Financ Econ 11:137–160, 2017), and we compare the dynamically optimal strategy with the precommitment one. While it is well known that the precommitment strategy is the solution to a target-based problem, we show that the same holds for the dynamically optimal strategy. In particular, the precommitment strategy has a constant target, while the dynamically optimal strategy has a time-varying target whose expectation coincides with the constant target of the previous case. We also show that the expected wealth is the same under the two approaches. Numerical applications show that (i) the median of the risky asset’s share is lower for the precommitment than the dynamically optimal strategy; (ii) the amount of money invested in the precommitment risky portfolio is highly more volatile than in the dynamically optimal case; (iii) the variance of wealth is lower with the precommitment strategy than with the dynamically optimal one; (iv) under scenarios of extreme market returns (either good or bad), the dynamically optimal strategy allows a more effective reaction because of the continuous adjustment of the final target.

De-risking long-term care insurance

In this paper, we propose a de-risking strategy model for LTC insurers facing with longevity and disability risks, by constructing hedge positions with vanilla disability swaps and options. We rely on long-term care insurance in a multiple state framework. The optimal hedge level for each de-risking strategies is computed, respectively, by minimizing the total cost of the de-risking strategy under the Conditional Value-at-Risk (CVaR) constraint on the total unfunded liabilities and minimizing the CVaR under a total cost constraint. A numerical application is performed, and the results suggest that a de-risking strategy based on disability derivatives can be a viable solution to reduce the portfolio riskiness of LTC insurers.

Portfolio performance analysis: a case study of cryptocurrencies

The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.

Portfolio performance analysis: a case study of cryptocurrencies

The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.

Fair estimation of capital risk allocation

Abstract In this paper, we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital allocations and provide explicit formulae for fair capital allocations in case when the constituents of the risky portfolio are jointly normally distributed. The main focus of the paper is on the problem of approximating fair portfolio allocations in the case of not fully known law of the portfolio constituents. We define and study the concepts of fair allocation estimators and asymptotically fair allocation estimators. A substantial part of our study is devoted to the problem of estimating fair risk allocations for expected shortfall. We study this problem under normality as well as in a nonparametric setup. We derive several estimators, and prove their fairness and/or asymptotic fairness. Last, but not least, we propose two backtesting methodologies that are oriented at assessing the performance of the allocation estimation procedure. The paper closes with a substantial numerical study of the subject and an application to market data.

Who Are the Bitcoin Investors? Evidence from Indirect Cryptocurrency Investments

Cryptocurrencies have received growing attention from individuals, the media, and regulators. However, little is known about the investors whom these financial instruments attract. Using administrative data, we describe the investment behavior of individuals who invest in cryptocurrencies with structured retail products. We find that cryptocurrency investors are active traders, prone to investment biases, and hold risky portfolios. In line with attention effects and anticipatory utility, we find that the average cryptocurrency investor substantially increases log-in and trading activity after his or her first cryptocurrency purchase. Our results document which investors are more likely to adopt new financial products and help inform regulators about investors' vulnerability to cryptocurrency investments.

Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA)

Growth-Trend (GT) timing from Philosophical Economics is a brilliant timing strategy which only signals a bear market when both the trend in the unemployment (UE) rate and the SP500 index are bearish. As a result, it captures most market downturns while switching to cash in less than 15% of the time. In this sense, its crash protection is much less drastic than our own “canary” protection in our DAA strategy (25% in cash) or the breadth protection in our VAA strategy (around 50% in cash). In this paper we apply GT timing to the well-known 60-40 static benchmark (60% SPY - 40% IEF), and search in-sample for variations on 60-40 with GT timing. For these variations, we in particular consider risky portfolios which are also agnostic for inflation and yield, inspired by the various static portfolio like the Permanent Portfolio and its siblings. Our final strategy switches between two static portfolios based on GT timing. This strategy is called the Lethargic Asset Allocation (LAA).

Portfolio models with return forecasting and transaction costs

In this paper, we advance portfolio models by incorporating return projection and further analyze their realized performance. To ensure practicality, the transaction costs and the optimization of short-selling weights are taken into account in portfolio rebalancing. Using the daily returns of international ETFs over a period of 14 years, the empirical results show that including return forecasting improves the realized performance due to more efficient asset allocation but not a reduction in trading costs. The models that are based on trade-off between return and volatility, such as the mean-variance and Omega models, show higher increases in performance than those mainly focus on controlling loss, such as the linearized value-at-risk, the conditional value-at-risk, and the downside risk. The superiority of forecasting risky portfolios over the equally-weighted diversification varies intertemporarily across various portfolio models. The benefit of inclusion of prediction is larger when the market is less volatile.