Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies

2015 ◽  
Vol 60 ◽  
pp. 1-20 ◽  
Author(s):  
Wolfgang Bessler ◽  
Dominik Wolff
Keyword(s):  
Investment Strategies ◽  
Sample Analysis ◽  
Out Of Sample

scholarly journals An approach to predict Spanish mortgage market activity using Google data

2019 ◽  
Vol 8 (4) ◽  
pp. 209
Author(s):  
Marcos González-Fernández ◽  
Carmen González-Velasco
Keyword(s):  
Prediction Accuracy ◽  
Mortgage Market ◽  
Sample Analysis ◽  
Market Activity ◽  
Out Of Sample

The aim of this paper is to use Google data to predict Spanish mortgage market activity during the period from January 2004 to January 2019. Thus, we collect monthly Google data for the keyword hipoteca, the Spanish expression for mortgage, and then, we perform a regression and an out-of-sample analysis. We find evidence that the use of Google data significantly improves prediction accuracy.

scholarly journals Enhanced indexation via chance constraints

2020 ◽  
Author(s):  
Patrizia Beraldi ◽  
Maria Elena Bruni
Keyword(s):  
Investment Strategy ◽  
Chance Constraints ◽  
Portfolio Performance ◽  
Investment Strategies ◽  
Index Tracking ◽  
Negative Deviation ◽  
Additional Risk ◽  
Benchmark Instances ◽  
Out Of Sample ◽  
The Empirical Analysis

Abstract The enhanced index tracking (EIT) represents a popular investment strategy designed to create a portfolio of assets that outperforms a benchmark, while bearing a limited additional risk. This paper analyzes the EIT problem by the chance constraints (CC) paradigm and proposes a formulation where the return of the tracking portfolio is imposed to overcome the benchmark with a high probability value. Besides the CC-based formulation, where the eventual shortage is controlled in probabilistic terms, the paper introduces a model based on the Integrated version of the CC. Here the negative deviation of the portfolio performance from the benchmark is measured and the corresponding expected value is limited to be lower than a given threshold. Extensive computational experiments are carried out on different set of benchmark instances. Both the proposed formulations suggest investment strategies that track very closely the benchmark over the out-of-sample horizon and often achieve better performance. When compared with other existing strategies, the empirical analysis reveals that no optimization model clearly dominates the others, even though the formulation based on the traditional form of the CC seems to be very competitive.

scholarly journals Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

2019 ◽  
Vol 8 (3) ◽  
pp. 485-493
Keyword(s):  
Performance Measures ◽  
Minimum Mean Square Error ◽  
Multivariate Garch ◽  
Sample Analysis ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
T Distribution

Forecasting plays a crucial role in determining the direction of future trends and in making necessary investment decisions. This research presents the forecasting performance of three multivariate GARCH models: SGARCH, EGARCH, and GJR-GARCH based on Gaussian and Student’s t-distribution. The forecasting ability of the models is evaluated on the basis of forecasting performance measures: MAE, SSE, MSE, and RMSE. This is done by examining the hedged portfolios of three indices of NSE: NIFTY50, BANKNIFTY, and NIFTYIT. Daily data from Jan 2006 to Dec 2017 is taken and forecasts are conducted using out of sample data from Jan 2016-Dec 2017. Minimum mean square error (MMSE) forecasting method is used to generate conditional variance and covariance forecasts which in turn generate hedge ratios and corresponding hedged portfolio. Minimum variance hedge ratio framework of Ederington (1979) is used for hedging. The in-sample analysis shows that SGARCH with both the distribution performed better than the other models while out-of-sample analysis provides mixed results. EGARCH model assigns the lowest hedge ratio to NIFTY50 and BANKNIFTY while SGARCH model assigns the lowest hedge ratio to NIFTYIT. Forecasting performance measures show the least value for SGARCH and EGARCH model. In future these models are able to reduce maximum risk from the spot market. The results of this research has important implications for financial decision and policy makers.

scholarly journals Improving the option pricing performance of GARCH models in inefficient market

2020 ◽  
Vol 17 (2) ◽  
pp. 14-25
Author(s):  
Noureddine Lahouel ◽  
Slaheddine Hellara
Keyword(s):  
Option Pricing ◽  
Market Efficiency ◽  
Real Data ◽  
Sample Analysis ◽  
New Approach ◽  
Out Of Sample ◽  
Garch Processes ◽  
The Difference ◽  
The Stability ◽  
Standard Risk

Understanding the relation between option pricing and market efficiency is important. Indeed, emphasizing this relation generates new insights that are appropriate in practice. These insights give a better understanding of the current limitations of the option pricing and hedging methods. This article thus aims to improve the performance of the option pricing approach. To start, the relation between the option pricing methodology and the informational market efficiency was discussed. It is, therefore, useful, before proceeding to apply the standard risk-neutral approach, to check the efficiency assumption. New modified GARCH processes were used to model the dynamics of the asset returns in the option pricing framework. The new considered approaches allow describing the dynamic of returns when the market is inefficient. Using real data on CAC 40 index, the performance of different models as a function of maturity and moneyness was studied. The in-sample analysis, interested in the stability of the pricing models across time, showed that the new approach, developed under the affine GARCH process, is the most accurate. The study of the out-of-sample performance, which aims to evaluate the forecasting ability of different approaches, confirmed the results of the in-sample analysis. For the optional portfolio hedging, always the best hedging approach is that obtained under the affine GARCH model. After a regression study, it was found that the difference between theoretical and observed option values can be explained by factors, which are not taken into account in the proposed pricing formulae.

Investment Strategies that Beat the Market. What Can We Squeeze from the Market?

e-Finanse ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 36-55
Author(s):  
Robert Ślepaczuk ◽  
Paweł Sakowski ◽  
Grzegorz Zakrzewski
Keyword(s):  
Risk Control ◽  
Financial Data ◽  
Investment Strategies ◽  
Sample Period ◽  
New Approach ◽  
Maximum Loss ◽  
Multi Stage ◽  
Daily Data ◽  
Out Of Sample ◽  
Maximum Drawdown

AbstractThe paper presents a new approach to optimizing automatic transactional systems. We propose a multi-stage technique which enables us to find investment strategies beating the market. Additionally, new measures of combined risk and returns are applied in the process of optimization. Moreover, we define new elements of a risk control system based on volatility measures and consecutive signal confirmation. As a result, we formulate three complex investment systems which maximize returns and simultaneously minimize risk in comparison to all other alternative investments (IR=2, Maximum Drawdown<21%, Maximum Loss Duration=0.75 year). Our analysis is based on historical daily data (1998-2010, in- and out-of-sample period) for index and commodity futures. Afterwards, the systems are reoptimized and reallocated each half a year in order to include the most recent financial data. Finally, we show the results for a joint model consisting of our three systems.

Analyst recommendations and stock price bubbles

2020 ◽  
Author(s):  
Karthik Balakrishnan ◽  
Catherine Schrand ◽  
Rahul Vashishtha
Keyword(s):  
Stock Price ◽  
Positive Association ◽  
Causal Factor ◽  
Analyst Recommendations ◽  
Positive Relation ◽  
Sample Analysis ◽  
Timely Manner ◽  
Price Bubbles ◽  
Out Of Sample ◽  
Future Return

This paper documents how analyst recommendations are related to periods of bubbles. We find a strong positive relation between the concentration in analyst buy recommendations and bubble continuation in two settings. First, we show a positive association between the concentration in buy recommendations and the tech bubble; the crash was associated with changes in buy recommendation concentration. Second, in an out-of-sample analysis of firms in multiple industries from 1994-2009, we show that analyst buy recommendation concentration predicts future return patterns that exhibit characteristics of a rational speculative bubble. While the evidence is not sufficient to conclude that analyst buy recommendations are the causal factor that perpetuates the mispricing, our findings suggest that, at a minimum, analysts do not act proactively to correct this form of mispricing in a timely manner.

scholarly journals Home and Motor insurance joined at a household level using multivariate credibility

2020 ◽  
pp. 1-33
Author(s):  
Florian Pechon ◽  
Michel Denuit ◽  
Julien Trufin
Keyword(s):  
Hierarchical Structure ◽  
Predictive Power ◽  
A Priori ◽  
Sample Analysis ◽  
Household Level ◽  
Insurance Policies ◽  
Motor Insurance ◽  
Out Of Sample ◽  
The Hierarchical Structure ◽  
The Impact

Abstract Actuarial ratemaking is usually performed at product and guarantee level, meaning that each product and guarantee is considered in isolation. Moreover, independence between policyholders is generally assumed. In this paper, we propose a multivariate Poisson mixture, with random effects correlated using a hierarchical structure, to accommodate for the dependence that may exist between unobserved risk factors across Home and Motor insurance and between policyholders from the same household. The hierarchical structure accounts for the fact that Home insurance covers the whole household, whereas Motor insurance policies are subscribed by specific policyholders within the household. The model allows to periodically correct the a priori expected claim frequencies using the reported number of claims in any of the considered products. Applications show that the impact of the number of claims reported in Motor insurance on the number of claims expected in Home insurance is larger than the other way around. Moreover, an out-of-sample analysis validates an improved predictive power. Also, the model allows to identify more rapidly the riskiest households.

scholarly journals Optimal Power Flow Under Uncertainty: An Extensive Out-of-Sample Analysis

2019 ◽  
Author(s):  
Adriano Arrigo ◽  
Christos Ordoudis ◽  
Jalal Kazempour ◽  
Zacharie de Greve ◽  
Jean-Francois Toubeau ◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Sample Analysis ◽  
Optimal Power ◽  
Out Of Sample

An Out-of-Sample Analysis of Investment Guarantees for Equity-Linked Products

2012 ◽  
Vol 16 (2) ◽  
pp. 183-206 ◽  
Author(s):  
Maciej Augustyniak ◽  
Mathieu Boudreault
Keyword(s):  
Sample Analysis ◽  
Out Of Sample

scholarly journals The Incidence of Spillover Effects during the Unconventional Monetary Policies Era

2021 ◽  
Vol 14 (6) ◽  
pp. 242
Author(s):  
Demetrio Lacava ◽  
Luca Scaffidi Domianello
Keyword(s):  
Financial Markets ◽  
Spillover Effects ◽  
Realized Volatility ◽  
Sample Analysis ◽  
Monetary Policies ◽  
Volatility Spillovers ◽  
Stock Indices ◽  
The Us ◽  
Out Of Sample ◽  
The One

In a context characterized by an increasing integration among financial markets, we aim to analyze whether the ECB unconventional monetary policy shields the Eurozone stock markets against spillovers of volatility from the US stock market. We augment the Markov switching Asymmetric Multiplicative Error Model (MS-AMEM) with exogenous variables to measure transmissions of volatility from the S&P500 index, on the one hand, and the announcement and implementation effects of unconventional policy, on the other hand. By estimating our model, the MS-AMEMX, on a sample of daily observations of the realized volatility of four Eurozone stock indices (CAC40, DAX30, FTSEMIB and IBEX35), we find how the increase in volatility brought about by volatility spillovers was mitigated by the implementation of unconventional policy, with a higher benefit for high-debt countries’ stock indices (FTSEMIB and IBEX35). Finally, the out-of-sample analysis certifies the suitability of our proxies also for forecasting purposes.

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