1,720,970 research outputs found
Stock market dispersion, the business cycle and expected factor returns
Full text linkWe provide evidence using data from the G7 countries suggesting that return dispersion may serve as an economic state variable in that it reliably predicts time-variation in economic activity, market returns, the value and momentum premia and market volatility. A relatively high return dispersion predicts a deterioration in business conditions, a higher value premium, a smaller momentum premium and lower market returns. Dispersion based market and factor timing strategies outperform out-of-sample buy and hold strategies. The evidence are robust to alternative specifications of return dispersion and are not driven by US data. Return dispersion conveys incremental information relative to idiosyncratic ris
Modeling Risk for Long and Short Trading Positions
Full text linkThe accuracy of parametric, non-parametric and semi-parametric methods in predicting the one-day-ahead Value-at-Risk (VaR) measure in three types of markets (stock exchanges, commodities and exchange rates) is investigated, both for long and short trading positions. The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance and power transformation of conditional variance.
Based on backtesting measures and a loss function evaluation method, we find out that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions.
Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns.
The behavior of the risk management techniques is examined both for long and short VaR trading positions, while to best of our knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, we implement a two-stage model selection in contrast of the most commonly used backtesting procedures in the attempt to identify a unique model. Finaly, we employ parametric, non-parametric and semi-parametric techniques in order to investigate their performance in a unify environment
Volatility forecasting: Intra-day versus inter-day models
No full textVolatility prediction is the key variable in forecasting the prices of options, value-at-risk and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of asset returns, but also intra-day models, we were able to investigate their forecasting performance for three European equity indices. A consistent relation is shown between the examined models and the specific purpose of volatility forecasts. Although researchers cannot apply one model for all forecasting purposes, evidence in favor of models that are based on inter-day datasets when their criteria based on daily frequency, such as value-at-risk and forecasts of option prices, are provided.
Backtesting VaR Models: A Τwo-Stage Procedure
Full text linkAcademics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets (US stock, gold and dollar-pound exchange rate markets), long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models
Global Style Portfolios Based on Country Indices
Full text linkFactor portfolios created by dynamically weighting country indices generated significant global market adjusted returns over the last thirty years. The comparison between stock and country based factor portfolios suggests that country based value, size and momentum factor portfolios implemented through index futures or country ETFs capture a large part of the return of stock based factor strategies. Given the complex issues and costs involved in implementing stock based factor strategies in practice, country based factor strategies offer a viable alternative. The behavior of the market and factor portfolios is dependent on the risk regime. A regime-dependent dynamic global factor portfolio outperforms the world equity market portfolio. The outperformance, in and out of sample, is robust to transaction costs and alternative portfolio construction methodologies
The efficiency of Greek public pension fund portfolios
No full textGreek public pension funds can invest up to 23% into risky assets and are not allowed to invest outside Greece. This paper seeks to investigate the costs of investment constraints on pension fund portfolios. In particular we try to quantify the losses that portfolios suffer due to under-diversification and sub-optimal asset allocation. We find that the high concentration of Greek equity portfolios imposes a substantial return and utility loss which is further increased when the lack of international diversification is taken into account. Restricting the weight of equities to 23% of the total portfolio, leads to sub-optimal asset allocation that costs as much as 2% (3%) per annum compared to a balanced domestic (global) benchmark.Portfolio efficiency Idiosyncratic risk Asset allocation Utility loss Pension funds
Idiosyncratic risk matters! A regime switching approach
No full textThe evidence on the inter-temporal relation between idiosyncratic risk and future stock returns is conflicting and confusing. We shed new light on the issue using a more flexible econometric approach based on [Hamilton, J.D. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357-384.] regime switching model that accommodates the parameter instability of the forecasting relation between returns and financial variables. We find strong evidence suggesting that idiosyncratic risk is related to future stock market returns only in the low variance regime.Idiosyncratic risk Stock market volatility Regime switching
Idiosyncratic volatility and equity returns: UK evidence
No full textThe proposition that idiosyncratic volatility may matter in asset pricing is currently a topic of research and controversy. Using data from the UK market we examine the predictive ability of various measures of idiosyncratic risk and provide evidence which suggests that: (a) it is the idiosyncratic volatility of small capitalization stocks that matters for asset pricing and (b) that small stocks idiosyncratic volatility predicts the small capitalization premium component of market returns and is unrelated to either the market or the value premium. The predictive power of the aggregate idiosyncratic volatility of small stocks remains intact even after we control for the possible proxying effects of business cycle fluctuations and liquidity and is robust across time and different econometric specifications.
Idiosyncratic risk, returns and liquidity in the London Stock Exchange: A spillover approach
No full textRecent evidence has shown that liquidity and idiosyncratic risk may be priced factors in the cross section of expected stock returns and that market capitalization significantly affects investor behavior and liquidity. We explore the interactions between liquidity, idiosyncratic risk and return across time as well as across size-based portfolios of stocks listed in the London Stock Exchange. We find that volatility spills over from large to small-cap stocks and vice versa and is predicted by illiquidity shocks in both small and large-cap portfolios. Illiquidity is forecasted by return shocks in small-cap stocks. Finally, we document some evidence of asymmetric liquidity spillovers, supporting the intuition that market-wide information is first incorporated in the trading behavior of large-cap investors and is then transmitted in the trading of small stocks.Illiquidity Idiosyncratic risk London stock exchange Spillovers
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