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on Financial Markets |
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Issue of 2022‒02‒21
nine papers chosen by |
| By: | Krämer-Eis, Helmut; Botsari, Antonia; Lang, Frank; Mandys, Filip |
| Abstract: | This working paper presents the results of the 2020 EIF Private Equity Mid-Market Survey, an anonymised online survey of the private equity (PE) mid-market general partner/management companies. Complementing the 2018 EIF Venture Capital Survey paper (2018/51), which examined the venture capital fund managers' perception of the EIF's value added, this paper analyses the sentiment of the PE mid-market fund managers towards the EIF, its products and processes, and value added. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:eifwps:202177&r= |
| By: | S{\o}ren Fiig Jarner |
| Abstract: | In this report we derive the strategic (deterministic) allocation to bonds and stocks resulting in the optimal mean-variance trade-off on a given investment horizon. The underlying capital market features a mean-reverting process for equity returns, and the primary question of interest is how mean-reversion effects the optimal strategy and the resulting portfolio value at the horizon. In particular, we are interested in knowing under which assumptions and on which horizons, the risk-reward trade-off is so favourable that the value of the portfolio is effectively bounded from below on the horizon. In this case, we might think of the portfolio as providing a stochastic excess return on top of a "guarantee" (the lower bound). Deriving optimal strategies is a well-known discipline in mathematical finance. The modern approach is to derive and solve the Hamilton-Jacobi-Bellman (HJB) differential equation characterizing the strategy leading to highest expected utility, for given utility function. However, for two reasons we approach the problem differently in this work. First, we wish to find the optimal strategy depending on time only, i.e., we do not allow for dependencies on capital market state variables, nor the value of the portfolio itself. This constraint characterizes the strategic allocation of long-term investors. Second, to gain insights on the role of mean-reversion, we wish to identify the entire family of extremal strategies, not only the optimal strategies. To derive the strategies we employ methods from calculus of variations, rather than the usual HJB approach. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.05375&r= |
| By: | W. Blake Marsh |
| Abstract: | I study investor responses to the 2020 bank stress tests that included restrictions on shareholder payouts. I find that banks subject to the stress tests and payout restrictions experienced both immediate and persistently lower excess stock price returns. In the cross-section, I find that excess stock returns declined with bank size but cannot otherwise be explained by pre-pandemic bank or payout characteristics, suggesting that investors penalized banks likely to experience greater regulatory scrutiny. However, the excess stock return penalties are smaller than those previously estimated in the literature examining voluntary payout reductions that signal bank distress. The results show that using supervisory discretion to take preventative actions during a crisis is less costly than waiting to take actions when banks are distressed. |
| Keywords: | Bank payout policy; Stress testing; Bank supervision |
| JEL: | G21 G28 G35 |
| Date: | 2022–01–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:93664&r= |
| By: | Bohdan M. Pavlyshenko |
| Abstract: | The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.02729&r= |
| By: | Josefin Meyer; Carmen M. Reinhart; Christoph Trebesch |
| Abstract: | This paper studies external sovereign bonds as an asset class. It compiles a new database of 266,000 monthly prices of foreign-currency government bonds traded in London and New York between 1815 (the Battle of Waterloo) and 2016, covering up to 91 countries. The main insight is that, as in equity markets, the returns on external sovereign bonds have been sufficiently high to compensate for risk. Real ex-post returns average more than 6 percent annually across two centuries, including default episodes, major wars, and global crises. This represents an excess return of 3-4 percent above US or UK government bonds, which is comparable to stocks and outperforms corporate bonds. Central to this finding are the high average coupons offered on external sovereign bonds. The observed returns are hard to reconcile with canonical theoretical models and the degree of credit risk in this market, as measured by historical default and recovery rates. Based on an archive of more than 300 sovereign debt restructurings since 1815, the authors show that full repudiation is rare; the median creditor loss (haircut) is below 50 percent. |
| Keywords: | Sovereign debt, return on investment, sovereign risk |
| JEL: | E4 F3 F4 G1 N0 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1993&r= |
| By: | Jinping Zhang; Keming Zhang |
| Abstract: | Risk management is very important for individual investors or companies. There are many ways to measure the risk of investment. Prices of risky assets vary rapidly and randomly due to the complexity of finance market. Random interval is a good tool to describe uncertainty with both randomness and imprecision. Considering the uncertainty of financial market, we employ random intervals to describe the returns of a risk asset and consider the tail risk, which is called the interval-valued Conditional Value at Risk (ICVaR, for short). Such an ICVaR is a risk measure and satisfies subadditivity. Under the new risk measure ICVaR, as a manner similar to the classical portfolio model of Markowitz, optimal interval-valued portfolio selection models are built. Based on the real data from mainland Chinese stock market, the case study shows that our models are interpretable and consistent with the practical scenario. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.02987&r= |
| By: | Andreas Fuster (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute; Centre for Economic Policy Research (CEPR)); David O. Lucca (Federal Reserve Banks - Federal Reserve Bank of New York); James I. Vickery (Federal Reserve Bank of Philadelphia) |
| Abstract: | This paper reviews the mortgage-backed securities (MBS) market, with a particular emphasis on agency residential MBS in the United States. We discuss the institutional environment, security design, MBS risks and asset pricing, and the economic effects of mortgage securitization. We also assemble descriptive statistics about market size, growth, security characteristics, prepayment, and trading activity. Throughout, we highlight insights from the expanding body of academic research on the MBS market and mortgage securitization. |
| Keywords: | mortgage finance, securitization, agency mortgage-backed securities, TBA, option-adjusted spreads, covered bonds. |
| JEL: | G10 G12 G21 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2213&r= |
| By: | Lööf, Hans (CESIS - Centre of Excellence for Science and Innovation Studies, Royal Institute of Technology); Sahamkhadam, Maziar (Linnaeus University); Stephan, Andreas (Linnaeus University) |
| Abstract: | This paper investigates how optimal portfolios of timber & forestry stocks perform relative to the global S&P timber & forestry index when corporate social responsibility (CSR) is considered. We incorporate CSR in the construction of optimal portfolios by utilizing environmental, social, and governance (ESG) scores. Historical as well as copula-augmented predictive models and ESG-constrained optimization are used to analyze out-of-sample performance of various portfolio strategies over the period 2018-2021. The results of copula-based portfolio strategies are better than of the historical models. Another insight gained by this study is that socially responsible investments in forestry stocks are feasible without sacrificing risk-adjusted returns. |
| Keywords: | portfolio optimization; ESG; forestry stocks; return; risk; vine copula |
| JEL: | G11 G12 G17 G32 |
| Date: | 2022–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:cesisp:0490&r= |
| By: | Jaydip Sen; Ashwin Kumar R S; Geetha Joseph; Kaushik Muthukrishnan; Koushik Tulasi; Praveen Varukolu |
| Abstract: | Stock price prediction is a challenging task and a lot of propositions exist in the literature in this area. Portfolio construction is a process of choosing a group of stocks and investing in them optimally to maximize the return while minimizing the risk. Since the time when Markowitz proposed the Modern Portfolio Theory, several advancements have happened in the area of building efficient portfolios. An investor can get the best benefit out of the stock market if the investor invests in an efficient portfolio and could take the buy or sell decision in advance, by estimating the future asset value of the portfolio with a high level of precision. In this project, we have built an efficient portfolio and to predict the future asset value by means of individual stock price prediction of the stocks in the portfolio. As part of building an efficient portfolio we have studied multiple portfolio optimization methods beginning with the Modern Portfolio theory. We have built the minimum variance portfolio and optimal risk portfolio for all the five chosen sectors by using past daily stock prices over the past five years as the training data, and have also conducted back testing to check the performance of the portfolio. A comparative study of minimum variance portfolio and optimal risk portfolio with equal weight portfolio is done by backtesting. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.05570&r= |