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on Financial Markets |
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Issue of 2016‒11‒06
nine papers chosen by |
| By: | Schneider, Paul; Wagner, Christian; Zechner, Josef |
| Abstract: | This paper shows theoretically and empirically that beta- and volatility-based low risk anomalies are driven by return skewness. The empirical patterns con- cisely match the predictions of our model which generates skewness of stock returns via default risk. With increasing downside risk, the standard capital as- set pricing model increasingly overestimates required equity returns relative to firms' true (skew-adjusted) market risk. Empirically, the profitability of betting against beta/volatility increases with firms' downside risk. Our results suggest that the returns to betting against beta/volatility do not necessarily pose asset pricing puzzles but rather that such strategies collect premia that compensate for skew risk. |
| Keywords: | low risk anomaly,skewness,credit risk,risk premia,equity options |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:550&r=fmk |
| By: | Nils Detering; Thilo Meyer-Brandis; Konstantinos Panagiotou; Daniel Ritter |
| Abstract: | To quantify and manage systemic risk in the interbank market, we propose a weighted, directed random network model. The vertices in the network are financial institutions and the weighted edges represent monetary exposures between them. Our model resembles the strong degree of heterogeneity observed in empirical data and the parameters of the model can easily be fitted to market data. We derive asymptotic results that, based on these parameters, allow to determine the impact of local shocks to the entire system and the wider economy. Furthermore, we characterize resilient and non-resilient cases. For networks with degree sequences without second moment, a small number of initially defaulted banks can trigger a substantial default cascade even under the absence of so called contagious links. Paralleling regulatory discussions we determine minimal capital requirements for financial institutions sufficient to make the network resilient to small shocks. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.09542&r=fmk |
| By: | Zhongxing Wang; Yan Yan; Xiaosong Chen |
| Abstract: | This paper looks into the analysis of the long-range auto-correlations and cross-correlations in bond market. Based on Detrended Moving Average (DMA) method, empirical results present a clear evidence of long-range persistence that exists in one year scale. The degree of long-range correlation related to maturities has an upward tendency with a peak in short term. These findings confirm the expectations of fractal market hypothesis (FMH). Furthermore, we have developed a method based on a complex network to study the long-range cross-correlation structure and apply it to our data, and found a clear pattern of market segmentation in the long run. We also detected the nature of long-range correlation in the sub-period 2007 to 2012 and 2011 to 2016. The result from our research shows that long-range auto-correlations are decreasing in the recent years while long-range cross-correlations are strengthening. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.09812&r=fmk |
| By: | Angela Cipollone (LUISS "Guido Carli" University); Paolo E. Giordani (LUISS "Guido Carli" University) |
| Abstract: | This paper estimates the process of search and matching between entrepreneurs and financiers in the business angel (BA) market. We hand-collect a new dataset from the BA markets of 17 developed countries for the period 1996-2014, and we estimate the aggregate matching function expressing the number of successful deals as a function of the number of potential entrepreneurs and of business angels. Empirical findings confirm the technological features assumed in the theoretical literature: positive and decreasing marginal returns to both inputs (stepping on toes effect), technological complementarity across the two inputs (thick market effect) and constant returns to scale. We discuss the theoretical and policy implications of these findings. |
| Keywords: | Entrepreneurial finance, innovation, matching function, business angels. |
| JEL: | C78 L26 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:lui:celegw:1601&r=fmk |
| By: | Ivan Guo; Gregoire Loeper |
| Abstract: | Derivatives on the Chicago Board Options Exchange volatility index (VIX) have gained significant popularity over the last decade. The pricing of VIX derivatives involves evaluating the square root of the expected realised variance which cannot be computed by direct Monte Carlo methods. Least squares Monte Carlo methods can be used but the sign of the error is difficult to determine. In this paper, we propose new model independent upper and lower pricing bounds for VIX derivatives. In particular, we first present a general stochastic duality result on payoffs involving concave functions. This is then applied to VIX derivatives along with minor adjustments to handle issues caused by the square root function. The upper bound involves the evaluation of a variance swap, while the lower bound involves estimating a martingale increment corresponding to its hedging portfolio. Both can be achieved simultaneously using a single linear least square regression. Numerical results show that the method works very well for VIX futures, calls and puts under a wide range of parameter choices. |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1611.00464&r=fmk |
| By: | Michaelides, Panayotis G.; Tsionas, Efthymios; Konstantakis, Konstantinos |
| Abstract: | The modeling process of bubbles, using advanced mathematical and econometric techniques, is a young field of research. In this context, significant model misspecification could result from ignoring potential non- linearities. More precisely, the present paper attempts to detect and date non- linear bubble episodes. To do so, we use Neural Networks tocapture the neglected non-linearities. Also, we provide a recursive dating procedure for bubble episodes. When using data on stock price-dividend ratio S&P500 (1871.1-2014.6), employing Bayesian techniques, the proposed approach identifies more episodes than otherbubble tests in the literature, while the common episodes are, in general, found to have a longer duration, which is evidence of an early warning mechanism (EWM) thatcouldhave important policy implications. |
| Keywords: | Bubbles, Non-linearities, Neural Networks, EWM, S&P500 |
| JEL: | G01 G17 G18 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:74477&r=fmk |
| By: | Taro Ikeda (Graduate School of Economics, Kobe University) |
| Abstract: | This note relumes fractal analysis on macroeconomics. We present a fractal market hypothesis for US stock prices. |
| Keywords: | Fractal geometry, Hurst exponent, market efficiency, chaos |
| JEL: | C18 E39 G14 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:koe:wpaper:1637&r=fmk |
| By: | Ana Isabel Ramos Domingues (FEP-UP, School of Economics and Management, University of Porto); António de Melo da Costa Cerqueira (FEP-UP, School of Economics and Management, University of Porto); Elísio Fernando Moreira Brandão (FEP-UP, School of Economics and Management, University of Porto) |
| Abstract: | Recently, the idiosyncratic volatility has captured much of the attention of the financial literature, being the idiosyncratic volatility puzzle one of the most studied. Our study aims to verify if the financial reporting quality, proxied by earnings quality, an accrual-based measure, has an impact on idiosyncratic return volatility, using as sample the firms listed on London Stock Exchange, and comprising the period between 1988 and 2015. To account for the robustness of our results, we used several control variables, such as leverage, size, ratio book-to-market, firm age and firm performance. We conclude that earnings quality has a positive impact on idiosyncratic volatility, meaning that poorer information quality implies higher idiosyncratic volatility. Posteriorly, we extend our study to a trend analysis, asking if the earnings quality behaviour is related with the idiosyncratic volatility trends. We prove that idiosyncratic volatility does not have a constant upward trend, instead it behaves like ebbs and flows. We found that earnings quality has an impact, albeit small, in the overall trend of idiosyncratic volatility, and also explains its episodic behaviour. |
| Keywords: | Idiosyncratic Volatility, Earnings Quality, Abnormal Accruals, Time Series Analysis |
| JEL: | G11 G12 G14 G32 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:por:fepwps:579&r=fmk |
| By: | Toshiyuki Sakiyama (Deputy Director, Economic and Financial Studies Division, Institute for Monetary and Economic Studies (currently Financial Markets Department), Bank of Japan (E-mail: toshiyuki.sakiyama@boj.or.jp)); Tetsuya Yamada (Director, Economic and Financial Studies Division, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: tetsuya.yamada@boj.or.jp)) |
| Abstract: | Recently, market liquidity in government bond markets has been attracting attention by market participants and central bankers since interest rate spikes have become frequent under unconventional monetary easing. We analyze network structures in the JGB (Japanese government bond) market using daily data from the BOJ-NET (the Bank of Japan Financial Network System). To our knowledge, this is the first network analysis on the government bond market. We studies how QQE (quantitative and qualitative monetary easing) has affected JGB market structure. We also conduct event studies for the spikes in interest rates (the shock after the introduction of QQE and the so-called VaR [Value at Risk] shock in 2003). In addition, we propose an agent-based model that accounts for the findings of the above event studies, and show that not only the capital adequacy of market participants but also the network structure are important for financial market stability. |
| Keywords: | Market Liquidity, Government bond markets, Quantitative and Qualitative Easing, Network analysis, Systemic risk, Agent-based model |
| JEL: | C58 G12 G18 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:ime:imedps:16-e-13&r=fmk |