Die Investition in Fine Wine unter Diversifikations- und Kostengesichtspunkten

2018 ◽  
Author(s):  
Thomas Nahmer
Keyword(s):  
Sharpe Ratio ◽  
Government Bond ◽  
Bond Index ◽  
World Government ◽  
Us Dollar ◽  
Sharpe Ratios

Dieses Papier untersucht die Sinnhaftigkeit von Fine Wine als Alternatives Investment unter besonderer Berücksichtigung der Kosten eines Fine Wine Investments. Ist Fine Wine zur weiteren Diversifizierung und damit zur Verbesserung des Risikio-Return-Profils von global in Aktien und Anleihen investierenden Portfolios geeignet? Die Analyse erfolgt in einem ersten Schritt auf Indexbasis und in einem zweiten Schritt auf Basis von realen Investitions-möglichkeiten. Die Referenzwährungen sind der US-Dollar und der Euro. Für die Indexbetrachtung werden auf der Aktienseite der MSCI-World-Index und für die Anleihen der JPM-World-Government-Bond-Index verwendet. Bei den Daten für die Investition in Fine Wine liegt der Fokus auf dem Liv-ex-50-Index der im Jahre 1999 gegründeten Londoner Weinbörse Liv-ex. Bei der realen Investition werden für die Datenanalyse bei Aktien und Anleihen Indexfonds verwendet. Da es für die Investition in Fine Wine keinen Indexfonds gibt, wird der Liv-ex-50-Index inklusive aller Kosten einer realen Investition berechnet. Es werden verschiedene Portfoliozusammensetzungen verglichen. Zum einen wird ein Portfolio aus 50% Aktien und 50% Anleihen einem Portfolio aus 45% Aktien, 45% Anleihen und 10% Fine Wine gegenübergestellt. Zum an-deren wird ein Portfolio aus 25% Aktien und 75% Anleihen gegen ein Portfolio aus 20% Aktien, 70% Anleihen und 10% Fine Wine gemessen. Als Vergleichsmaßstab werden die annualisierte Rendite, die Standardabweichung sowie das Sharpe-Ratio der jeweiligen Portfolios berechnet. Die Ergebnisse für die genannten Zeiträume sind ernüchternd. Die Beimischung von Fine Wine führt auf Indexebene lediglich zu einer leichten Verbesserung der annualisierten Rendite aber zu einer markanten Erhöhung des Risi-kos. Bei der Betrachtung der realen Investition kommen die hohen Kosten eines Investments in Fine Wine zum Tragen. Die annualisierte Rendite ist im Vergleich zu den Portfolios ohne Beimischung von Fine Wine niedriger bei gleichzeitig höheren Risikowerten. Lediglich bei der Betrachtung auf Indexbasis in Euro kann bei einem Portfolio eine leichte Verbesserung der Sharpe-Ratio verzeichnet werden. Bei der Betrachtung nach Kosten führt in allen Fällen die Beimischung von Fine Wine zu einer Verschlechterung der Sharpe-Ratios.

The Impact of Volatility Targeting

2018 ◽  
Keyword(s):  
Sharpe Ratio ◽  
Leverage Effect ◽  
Left Tail ◽  
Risk Parity ◽  
Asset Classes ◽  
Equity Portfolios ◽  
Sharpe Ratios ◽  
Tail Events ◽  
The Impact ◽  
Extreme Returns

Recent studies show that volatility-managed equity portfolios realize higher Sharpe ratios than portfolios with a constant notional exposure. The authors show that this result only holds for risk assets, such as equity and credit, and they link this finding to the so-called leverage effect for those assets. In contrast, for bonds, currencies, and commodities, the impact of volatility targeting on the Sharpe ratio is negligible. However, the impact of volatility targeting goes beyond the Sharpe ratio: It reduces the likelihood of extreme returns across all asset classes. Particularly relevant for investors, left-tail events tend to be less severe because they typically occur at times of elevated volatility, when a target-volatility portfolio has a relatively small notional exposure. We also consider the popular 60–40 equity–bond balanced portfolio and an equity–bond–credit–commodity risk parity portfolio. Volatility scaling at both the asset and portfolio level improves Sharpe ratios and reduces the likelihood of tail events.

scholarly journals An Index of the Yields of Junk Bonds, 1910–1955

2017 ◽  
Vol 77 (4) ◽  
pp. 1203-1219 ◽  
Author(s):  
Peter Basile ◽  
Sung Won Kang ◽  
John Landon-Lane ◽  
Hugh Rockoff
Keyword(s):  
Twentieth Century ◽  
High Risk ◽  
Corporate Bond ◽  
High Yield ◽  
Government Bond ◽  
Bond Yields ◽  
Middle Years ◽  
Bond Index ◽  
Junk Bonds

We present a new monthly index of the yields on junk bonds (high risk, high yield bonds) for the period 1910–1955. This index supplements the indexes of government bond yields, and Aaa and Baa corporate bond yields economic historians have relied on previously to describe the long-term risk spectrum. First, we describe our sources and methods. Then we show that our junk bond index contains information that is not in the closest alternative, and suggest some ways that the junk bond index could be used to enrich our understanding of the turbulent middle years of the twentieth century.

Sharpe Ratios and Alphas in Continuous Time

2004 ◽  
Vol 39 (1) ◽  
pp. 103-114 ◽  
Author(s):  
Lars Tyge Nielsen ◽  
Maria Vassalou
Keyword(s):  
Continuous Time ◽  
Sharpe Ratio ◽  
Continuous Time Model ◽  
Time Model ◽  
Time Performance ◽  
Optimal Portfolio Selection ◽  
Dynamic Portfolio ◽  
Sharpe Ratios ◽  
The One ◽  
Riskless Asset

AbstractThis paper proposes modified versions of the Sharpe ratio and Jensen's alpha, which are appropriate in a simple continuous-time model. Both are derived from optimal portfolio selection. The modified Sharpe ratio equals the ordinary Sharpe ratio plus half of the volatility of the fund. The modified alpha also differs from the ordinary alpha by a second-moment adjustment. The modified and the ordinary Sharpe ratios may rank funds differently. In particular, if two funds have the same ordinary Sharpe ratio, then the one with the higher volatility will rank higher according to the modified Sharpe ratio. This is justified by the underlying dynamic portfolio theory. Unlike their discrete-time versions, the continuous-time performance measures take into account that it is optimal for investors to change the fractions of their wealth held in the fund vs. the riskless asset over time.

scholarly journals The Influence Of Higher Moments And Non-Normality On The Sharpe Ratio: A South African Perspective

2014 ◽  
Vol 31 (1) ◽  
pp. 197
Author(s):  
Chris Van Heerden
Keyword(s):  
South African ◽  
Building Blocks ◽  
Reference Intervals ◽  
General Assumption ◽  
Sharpe Ratio ◽  
Higher Moments ◽  
Crisis Period ◽  
Monthly Return ◽  
African Perspective ◽  
Sharpe Ratios

Although the general assumption is that daily and monthly return data are normally distributed (Aparicio & Estrada, 2001), the correct statistical distribution of returns must first be established (Linden, 2001), as it constitutes one of the elementary building blocks that will ensure accurate financial analyses (Taylor, 1986). The assumption of normality is also critical when constructing reference intervals for variables (Royston, 1991). By evaluating the pre-, during and post- 2007-2009 financial crisis periods, this paper found that non-normality can be present in all data frequencies, especially in higher data frequencies. Further evidence also illustrated that the deviation from normality escalated over the crisis period and remained higher after the crisis, compared to the pre-crisis period. By comparing the traditional Sharpe ratio with adjusted versions, based on Gatfaouis (2012) methodology, this paper accentuates that the presence of non-normality and higher moments can influence the Sharpe ratios performance rankings.

scholarly journals BOND MARKET EFFICIENCY AND VOLATILITY: EVIDENCE FROM RUSSIA

2019 ◽  
Vol 7 (4) ◽  
pp. 1389-1397
Author(s):  
Shadi Omran ◽  
Elena Semnkova
Keyword(s):  
Market Efficiency ◽  
Unit Root Test ◽  
Weak Form ◽  
Bond Market ◽  
Corporate Bond ◽  
Good Representation ◽  
Bond Markets ◽  
Government Bond ◽  
Time Model ◽  
Bond Index

Purpose of the study: In this paper, we use daily return for the Moscow Exchange Government Bond index (RGBITR) and Moscow Exchange Corporate Bond index (MICEXCBITR) over the period 2013 to 2018. Methodology: Normality test, unit root test (ADF) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model will be used in this paper. Results: The empirical results reveal that both government and corporate bond markets in Russia are not weak-form efficient. Furthermore, the volatility is persistent in both bond indices and resembles the same movement in returns. We find also that the GARCH (1,1) model is a good representation of the behavior of daily bond index returns in corporate and government bond markets in Russia. Applications of this study: This research can be used for the universities, teachers, and students. Novelty/Originality of this study: In this paper, for the first-time model of bond market efficiency and volatility has been studied.

scholarly journals Sharpe (Ratio) Thinking about the Investment Opportunity Set and CAPM Relationship

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Valeriy Zakamulin
Keyword(s):  
Correlation Matrix ◽  
Optimization Procedure ◽  
Sharpe Ratio ◽  
Portfolio Performance ◽  
Investment Opportunity ◽  
Risky Assets ◽  
Comparative Static Analysis ◽  
Investment Opportunity Set ◽  
Alternative Characterization ◽  
Sharpe Ratios

In the presence of a risk-free asset the investment opportunity set obtained via the Markowitz portfolio optimization procedure is usually characterized in terms of the vector of excess returns on individual risky assets and the variance-covariance matrix. We show that the investment opportunity set can alternatively be characterized in terms of the vector of Sharpe ratios of individual risky assets and the correlation matrix. This implies that the changes in the characteristics of individual risky assets that preserve the Sharpe ratios and the correlation matrix do not change the investment opportunity set. The alternative characterization makes it simple to perform a comparative static analysis that provides an answer to the question of what happens with the investment opportunity set when we change the risk-return characteristics of individual risky assets. We demonstrate the advantages of using the alternative characterization of the investment opportunity set in the investment practice. The Sharpe ratio thinking also motivates reconsidering the CAPM relationship and adjusting Jensen's alpha in order to properly measure abnormal portfolio performance.

scholarly journals New Evidence On Hedge Fund Performance Measures

2011 ◽  
Vol 8 (11) ◽  
Author(s):  
Komlan Sedzro
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Measures ◽  
Hedge Funds ◽  
Hedge Fund ◽  
Fund Performance ◽  
Sharpe Ratio ◽  
Data Envelopment ◽  
Sharpe Ratios ◽  
Hedge Fund Performance ◽  
New Evidence

Hedge funds are still relatively unfamiliar to most investors despite the intense popularity they have enjoyed in recent years. Measuring the performance of these financial instruments using traditional methods is, however, problematic, since their returns do not follow a normal distribution. In this study, we consider rankings obtained with the Stochastic Dominance (SD) method and compare them with ranks produced using Sharpe Ratios, Modified Sharpe Ratios, and Data Envelopment Analysis. We also explore the advantages highlighted by the literature of the Data Envelopment Analysis (DEA) method in relation to traditional measures like Sharpe ratio and Modified Sharpe ratio. Our results show that classic performance measures are better correlated with SD than DEA results.

scholarly journals The Bias Ratio As A Hedge Fund Fraud Indicator: An Empirical Performance Study Under Different Economic Conditions

2014 ◽  
Vol 13 (4) ◽  
pp. 867 ◽  
Author(s):  
Francois Van Dyk ◽  
Gary Van Vuuren ◽  
Andre Heymans
Keyword(s):  
Hedge Funds ◽  
Performance Metrics ◽  
Estimation Error ◽  
Investment Decision ◽  
Hedge Fund ◽  
Sharpe Ratio ◽  
Performance Study ◽  
Additional Information ◽  
Bias Ratio ◽  
Sharpe Ratios

The Sharpe ratio is widely used as a performance evaluation measure for traditional (i.e., long only) investment funds as well as less-conventional funds such as hedge funds. Based on mean-variance theory, the Sharpe ratio only considers the first two moments of return distributions, so hedge funds characterised by complex, asymmetric, highly-skewed returns with non-negligible higher moments may be misdiagnosed in terms of performance. The Sharpe ratio is also susceptible to manipulation and estimation error. These drawbacks have demonstrated the need for augmented measures, or, in some cases, replacement fund performance metrics. Over the period January 2000 to December 2011 the monthly returns of 184 international long/short (equity) hedge funds with investment mandates that span the geographical areas of North America, Europe, and Asia were examined. This study compares results obtained using the Sharpe ratio (in which returns are assumed to be serially uncorrelated) with those obtained using a technique which does account for serial return correlation. Standard techniques for annualising Sharpe ratios, based on monthly estimators, do not account for serial return correlation this study compares Sharpe ratio results obtained using a technique which accounts for serial return correlation. In addition, this study assess whether the Bias ratio supplements the Sharpe ratio in the evaluation of hedge fund risk and thus in the investment decision-making process. The Bias and Sharpe ratios were estimated on a rolling basis to ascertain whether the Bias ratio does indeed provide useful additional information to investors to that provided solely by the Sharpe ratio.

scholarly journals Time-Varying Sharpe Ratios and Market Timing

2011 ◽  
Vol 01 (03) ◽  
pp. 465-493 ◽  
Author(s):  
Yi Tang ◽  
Robert F. Whitelaw
Keyword(s):  
Time Variation ◽  
Market Timing ◽  
Sharpe Ratio ◽  
Equity Returns ◽  
Conditional Mean ◽  
Financial Variables ◽  
Out Of Sample ◽  
The Business Cycle ◽  
Sharpe Ratios ◽  
Timing Strategies

This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined financial variables are used to estimate both the conditional mean and volatility of equity returns, and these moments are combined to estimate the conditional Sharpe ratio, or the Sharpe ratio is estimated directly as a linear function of these same variables. In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value. In spite of the well-known predictability of volatility and the more controversial forecastability of returns, it is the latter factor that accounts primarily for both the in-sample and out-of-sample results.

scholarly journals Portfolio management system in equity market neutral using reinforcement learning

2021 ◽  
Author(s):  
Mu-En Wu ◽  
Jia-Hao Syu ◽  
Jerry Chun-Wei Lin ◽  
Jan-Ming Ho
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Portfolio Management ◽  
Stock Prices ◽  
Management System ◽  
Sharpe Ratio ◽  
Reward Function ◽  
Portfolio Strategies ◽  
Sharpe Ratios ◽  
Almost All

AbstractPortfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the . Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

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