The Conditional CAPM Does Not Explain Asset-Pricing Anomalies

High Idiosyncratic Volatility and Low Returns: International and Further U.S. Evidence

This paper examines the volatility of common stocks of the Athens Stock Exchange at the market, industry and firm level. Over the period from 1988 to 2009 there has been a considerable increase in firm-level volatility relative to the market volatility, which implies that it takes increasingly more stocks to diversify away idiosyncratic risk. All volatility series move together and they are trended upwards. All three volatility measures show a countercyclical behaviour relative to GDP growth, and they all help to forecast GDP. Correlations among individual stocks have increased over the sample period, yet the explanatory power of the market model for a typical stock is still relatively low. All three volatility series rise during times of low returns. Factors that may be responsible for these findings are suggested.

Have individual stocks become more volatile : an empirical exploration of idiosyncratic risk

International Asset Allocation with Regime Shifts

Market Microstructure and Asset Pricing: On the Compensation for Illiquidity in Stock Returns

We evaluate the performance of an extensive family of ARCH models in modelling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.

The Use of GARCH Models in VaR Estimation

The use of GARCH models in VaR estimation

This paper proposes a method of calculating a Liquidity Adjusted Value-at-Risk (L-VaR) measure. The traditional approaches that have been implemented assume that the financial markets are perfect and hence an investor can either buy or sell any amount of stock without causing significant price changes. However, this conjecture is not a realist one as most of the markets, especially the emerging ones, are illiquid. In the attempt to create a L-VaR measure that accounts for the spread variation, we estimate the components of the bid-ask spread in order to calculate accurately both the endogenous and the exogenous liquidity risk. Under the new framework, the liquidation price of a position will not be the midpoint of the spread, but at least the bid price and therefore the calculated Value-at-Risk number will be more realistic. We extend the Madhavan et al. (1997) model by incorporating the traded volume and find out that both the adverse selection component and the L-VaR measure exhibit a U-shape pattern throughout the day, while the percent of risk that is attributed to liquidity displays an inverse U-shape pattern. Finally, at higher confidence level, the liquidity component of the high-priced, high-capitalization stocks represents 3.40% of the total market risk, while for the low capitalization securities it equals to 11% and therefore cannot be neglected.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID661281_code362451.pdf?abstractid=661281&rulid=5319822&mirid=2

Liquidity Adjusted Value-at-Risk Based on the Components of the Bid-ask Spread

/pdf/volatility-forecasting-intra-day-versus-inter-day-models-1i6k1batyz.pdf

Volatility forecasting: intra-day versus inter-day models

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark results in different measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence fund performance evaluation utilizing stock selection and timing measures that incorporate the self-reported benchmark. We introduce a new factor exposure based approach for measuring the – static and dynamic – timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be misstating skill because they ignore the managers’ self-reported benchmark in the performance evaluation process.

/pdf/revisiting-mutual-fund-performance-evaluation-4b28kgim4n.pdf

Timotheos Angelidis

Papers

The Use of GARCH Models in VaR Estimation

The use of GARCH models in VaR estimation

Liquidity Adjusted Value-at-Risk Based on the Components of the Bid-ask Spread

Volatility forecasting: intra-day versus inter-day models

Revisiting Mutual Fund Performance Evaluation