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Many financial instruments are designed with embedded leverage, such as options and leveraged exchange-traded funds (ETFs). Embedded leverage alleviates investors’ leverage constraints, and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find empirically that options and leveraged ETFs provide significant amounts of embedded leverage; this embedded leverage increases return volatility in proportion to the embedded leverage; and higher embedded leverage is associated with lower risk-adjusted returns. The results are statistically and economically significant, and we provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics. (JEL G02, G11, G12, G13, G14, G20)

We study three generic clearing arrangements in the presence of two‐sided limited commitment: simple bilateral clearing, segregated collateral clearing through a third party, and—most sophisticated—central counterparty (CCP) clearing. Clearing secures the settlement of obligations from over‐the‐counter forward contracts that smooth the income of risk‐averse traders. Clearing requires collateral to guarantee settlement; this is costly, as it reduces income from investment. More sophisticated clearing arrangements require more collateral. As a result, the welfare gains of CCP clearing may be mostly due to segregation, while mutualization of losses could contribute little to welfare.

This work uses financial markets connected by arbitrage relations to investigate the dynamics of price and liquidity discovery, which refer to the cross-instrument forecasting power for prices and liquidity, respectively. Specifically, we seek to understand the linkage between the cheapest to deliver bond and closest futures pairs by using high-frequency data on European governments obligations and derivatives. We split the 2019-2021 sample into three subperiods to appreciate changes in the liquidity discovery induced by the COVID-19 pandemic. Within a cointegration model, we find that price discovery occurs on the futures market, and document strong empirical support for liquidity spillovers both from the futures to the cash market as well as from the cash to the futures market.

Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible, trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC--USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent trading frictions associated with short sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC--USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.

We introduce a canonical representation of call options, and propose a solution to two open problems in option pricing theory. The first problem was posed by (Kassouf, 1969, pg. 694) seeking “theoretical substantiation” for his robust option pricing power law which eschewed assumptions about risk attitudes, rejected risk neutrality, and made no assumptions about stock price distribution. The second problem was posed by (Scott, 1987, pp. 423-424) who could not find a unique solution to the call option price in his option pricing model with stochastic volatility–without appealing to an equilibrium asset pricing model by Hull and White (1987), and concluded: “[w]e cannot determine the price of a call option without knowing the price of another call on the same stock”. First, we show that under certain conditions derivative assets are superstructures of the underlying. Hence any option pricing or derivative pricing model in a given number field, based on an anticipating variable in an extended field, with coefficients in a subfield containing the underlying, is admissible for market timing. For the anticipating variable is an algebraic number that generates the subfield in which it is the root of an equation. Accordingly, any polynomial which satisfies those criteria is admissible for price discovery and or market timing. Therefore, at least for empirical purposes, elaborate models of mathematical physics or otherwise are unnecessary for pricing derivatives because much simpler adaptive polynomials in suitable algebraic numbers are functionally equivalent. Second, we prove, analytically, that Kassouf (1969) power law specification for option pricing is functionally equivalent to Black and Scholes (1973); Merton (1973) in an algebraic number field containing the underlying. In fact, we introduce a canonical polynomial representation theory of call option pricing convex in time to maturity, and algebraic number of the underlying–with coefficients based on observables in a subfield. Thus, paving the way for Wold decomposition of option prices, and subsequently laying a theoretical foundation for a GARCH option pricing model. Third, our canonical representation theory has an inherent regenerative multifactor decomposition of call option price that (1) induces a duality theorem for call option prices, and (2) permits estimation of risk factor exposure for Greeks by standard [polynomial] regression procedures. Thereby providing a theoretical (a) basis for option pricing of Greeks, and (b) solving Scott’s dual call option problem a fortiori with our duality theory in tandem with Riesz representation theory. Fourth, when the Wold decomposition procedure is applied we are able to construct an empirical pricing kernel for call option based on residuals from a model of risk exposure to persistent and transient risk factors. Keywords: number theory; price discovery; derivatives pricing; asset pricing; canonical representation; Wold decomposition; empirical pricing kernel; option Greeks; dual option pricing

In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a number of very important processes in finance. We then obtain an estimation for the distribution of hedging error by maximising Shannon’s entropy subject to a set of moment constraints, which in turn yields the value-at-risk and expected shortfall of the hedging error. The significance of this approach lies in the fact that the maximum entropy estimator allows us to obtain a consistent estimate of the asymptotic distribution of hedging error, despite the non-normality of the underlying distribution of returns.

Using the pandemic as a laboratory, we show that asset markets assign a time- varying price to firms' disaster risk exposure. In 2020 the cross-section of realized and expected stock returns reflected firms' different exposure to the pandemic, as measured by their vulnerability to social distancing. Realized and expected return differentials initially widened and then narrowed, but disaster exposure still commanded a risk premium in December 2020. When inferred from market outcomes, resilience correlates not only with social distancing, but also with cash and environmental ratings. However, vulnerability to social distancing is the only characteristic that identifies persistently scarred firms.

PhD (Business Administration), North-West University, Mafikeng Campus Liquidity dried up in the 2007-2009 global financial crisis as banks were not able to borrow money from each other or either obtain money from the inter-market, which led to the closure and bailing out of various financial institutions. Financial regulation and liquidity risk management are critical to financial stability and to the economy as a whole. This prompted a call to revise banking regulations and risk management, which subsequently led the Basel Committee to introduce the Basel III, which encompasses liquidity measurements. The main purpose of this thesis was to analyse commercial banks’ liquidity shocks in Namibia for the period 2009 to 2018 using the Structural VAR model (SVAR). The aim of the study was to be achieved by satisfying two sub-objectives, namely, conducting an audit of the liquidity data between 2009 and 2018, and exploring how banks were exposed to liquidity risk. Secondly, the study sought to determine the extent to which the Net Stable Funding Ratio (NSFR) which is part of Basel III can strengthen Uniform Financial Institution Rating system which is referred to by the acronym CAMELS indicators in identifying liquidity shocks. The sample consisted of four of the largest Namibian commercial banks representing 98% of total assets. These banks are namely Standard Bank, First National Bank, Nedbank and Bank Windhoek. The data sources were balance sheets that were derived from the Bank of Namibia. In addition, the Namibia Statistics Agency provided macroeconomic indicators. The empirical analysis covered the most recent global financial crisis (2007-2009) and the post crisis period, which was dominated by the shortage of liquidity in banks. Using SVAR, the study analysed the interactions of macroeconomic variables in identifying the structural shocks and provided some implications for policy and decision-making. In estimating the SVAR regression model, granger causality, impulse-response functions and forecast error variance decomposition were employed and evaluated. By auditing liquidity data between 2009 and 2018, empirical results showed that commercial banks liquidity risk in is caused by a combination of structural shocks. The granger causality, impulse-response functions and forecast error variance decomposition documented that credit risk (non-performing loans) and sensitivity to market risk (RSA_RSL) are key factors affecting commercial banks liquidity conditions in Namibia in the medium to long run. In addition, the empirical results showed that quality earnings (ROA) and capital adequacy (Tier 1 RWCR) have minimal impacts on liquidity conditions in the short run. By determining the extent to which the NSFR can strengthen CAMELS indicators in identifying liquidity shocks, the findings imply that the NSFR has minimal influence in strengthening the traditional indicators based on the CAMELS approach in identifying the liquidity shocks. The findings support the financial regulations’ focus on the traditional CAMELS approach in identifying the liquidity shocks. The study contributes to the empirical research by establishing the relationship between non-performing loans ratio (NPL) and two liquidity ratios, namely, Liquid assets to total assets (LA_TA) and Liquid asset to average total liabilities ratio (LA_ATL) as sources of liquidity shocks in banks. This is the first study to examine the relationship between NPL, LA_TA and LA_ATL from the present researcher’s knowledge and the study helped to establish the said relationships. Additionally, this study also established a relationship between rate sensitivity to assets and liabilities (RSA_RSL) and LA_TA. Again, from the present researcher’s knowledge, there is no other study that has attempted to establish the said relationships. On the other hand, the study created and used disaggregated data in the case of Net Stable Funding Ratio to demystify the relationship with other CAMELS ratios. Reforming assets quality policies, rate to sensitivity to market risk policies, earnings quality policies, capital adequacy policies, and liquidity policies such as Basel III liquidity measures can be valuable policy tools to minimise liquidity shortages and avoid insolvent banks in Namibia. Doctoral

This paper argues that the reduced-form jump diffusion model may not be appropriate for credit risk modeling. To correctly value hybrid defaultable financial instruments, e.g., convertible bonds, we present a new framework that relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically, however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price., https://ia801504.us.archive.org/8/items/pricingConvertibleBondJumpDiffusion-5/pricingConvertibleBondJumpDiffusion-5.pdf

Equity basket correlation is an important risk factor. It characterizes the strength of linear dependence between assets and thus measures the degree of portfolio diversification. It can be estimated both under the physical measure from return series, and under the risk neutral measure from option prices. The difference between the two estimates motivates a so called "dispersion strategy". We study the performance of this strategy on the German market over the recent 2 years and propose several hedging schemes based on implied correlation (IC) forecasts. Modeling IC is a challenging task both in terms of computational burden and estimation error. First the number of correlation coefficients to be estimated would grow with the size of the basket. Second, since the IC is implied from option prices it is not constant over maturities and strikes. Finally, the IC changes over time. The dimensionality of the problem is reduced by an assumption that the correlation between all pairs of equities is constant (equicorrelation). The IC surface (ICS) is then approximated from implied volatilities of stocks and implied volatility of the basket. To analyze this structure and the dynamics of the ICS we employ a dynamic semiparametric factor model (DSFM).

We present a novel empirical benchmark for analyzing credit risk using “pseudo firms” that purchase traded assets financed with equity and zero-coupon bonds. By no-arbitrage, pseudo bonds are equivalent to Treasuries minus put options on pseudo firm assets. Empirically, like corporate spreads, pseudo bond spreads are large, countercyclical, and predict lower economic growth. Using this framework, we find that bond market illiquidity, investors' overestimation of default risks, and corporate frictions do not seem to explain excessive observed credit spreads but, instead, a risk premium for tail and idiosyncratic asset risks is the primary determinant of corporate spreads. (JEL E23, E32, E44, G13, G24, G32)

We develop a theoretical framework for covariance stationary but persistent positively valued processes which combines a semi-nonparametric expansion of the Gamma distribution with a component version of the multiplicative error model. Our conditional mean assumption allows for slow, possibly nonmonotonic mean-reversion, while our distribution assumption provides more flexibility than a traditional Laguerre expansion while preserving positivity of the density. We apply our framework to a dynamic portfolio allocation for Exchange Traded Notes tracking short- and mid-term VIX futures indices, which are increasingly popular but risky financial instruments. We show the superior performance of the strategies based on our econometric model.

We investigate the importance of ambiguity, or Knightian uncertainty, in executives' decisions about when to exercise stock options. We develop an empirical estimate of ambiguity and include it in regression models alongside the more traditional measure of risk, equity volatility. We show that each variable has a statistically significant effect on the timing of option exercises, with volatility causing executives to hold their options longer in order to preserve remaining option value, and ambiguity increasing the tendency for executives to exercise early in response to risk aversion. Regression estimates for the volatility and ambiguity variables imply similar magnitudes of economic impact upon the exercise decision, with the volatility variable being about 2.5 times stronger.

The LIBOR Market Model has become one of the most popular models for pricing interest rate products. It is commonly believed that Monte-Carlo simulation is the only viable method available for the LIBOR Market Model. In this article, however, we propose a lattice approach to price interest rate products within the LIBOR Market Model by introducing a shifted forward measure and several novel fast drift approximation methods. This model should achieve the best performance without losing much accuracy. Moreover, the calibration is almost automatic and it is simple and easy to implement. Adding this model to the valuation toolkit is actually quite useful; especially for risk management or in the case there is a need for a quick turnaround., https://ia801403.us.archive.org/29/items/lattice-in-lmm-4/Lattice-in-LMM-4.pdf

In this paper we set out to test whether, on sector level, returns series in South Africa exhibit long memory and asymmetries and, more specifically, whether these effects should be accounted for when assessing downside risk. The purpose of this analysis is not to identify the most optimal downside risk assessment model or to reaffirm the often regarded stylized fact of long memory and asymmetry in asset returns series. Rather we set out to establish whether accounting for these effects and allowing for more flexibility in second order persistence models lead to improved risk assessments. We use several variants of the widely used GARCH family of second order persistence models that control for these effects, and compare the downside risk estimates using Value-at-Risk measures of these different models and compare their out-of-sample performances. Our findings confirm that controlling for asymmetries and long memory in volatility models improve risk management calculations.

This paper provides an assessment of the comparative effectiveness of four econometric methods in estimating the optimal hedge ratio in an emerging equity market, particularly the South African equity and futures markets. The paper bases the effectiveness of hedging on volatility reduction and minimisation of the coefficient of variation of hedged returns as well as risk-aversion based utility maximisation. The empirical analysis shows that the single equation method estimated by ordinary least squares is the most effective over daily hedging periods. However, the vector error-correction method and multivariate GARCH methods are most effective over weekly and monthly hedging periods.

International audience; This research provides an alternative framework for the analysis of employee stock option exercise patterns. It develops a binomial model where the exercise decision obeys to a policy that maximizes the expected utility to a representative employee exhibiting preferences as described by the Cumulative Prospect Theory (CPT). Using a large database on exercise transactions in 12 US public corporations, I examined the performance of the model in predicting actual exercise patterns. Interestingly, the probability weighting coefficients yielded by the model calibration are consistent with those from the experimental literature. Further, the results suggest that the model outperforms the Expected Utility Theory-based model in predicting actual exercise decisions in the sample. These findings convey the main contribution of this paper: the strong ability of the CPT framework to explain employees exercise behavior. It therefore provides rationale for using this framework in order to get more relevant fair value estimates of stock options.

This article reviews recent econometric developments in the literature on price discovery and predictability. For both areas, we discuss traditional approaches to econometric modeling, limitations to these approaches, and recent developments designed to overcome them. We also discuss the state of the art and suggest future research. Three main conclusions are drawn. First, while many recent empirical applications in price discovery and price predictability are on the frontier of econometric methods, further developments are needed to increase relaxation of relevant assumptions and push the boundaries of applications. Second, future research in econometric modeling needs to combine/synthesize recent developments across multiple econometric issues, rather than proceeding in a piecemeal manner, for instance, by integrating developments in the time series literature into panel-based frameworks. Third, recent econometric literature is generating findings that challenge long-held beliefs about apparent empirical regularities in price discovery and price predictability, thus presenting opportunities to develop relevant theory.

Using a sample of 161 global banks in 23 countries, we examine the applicability of market-based structural models and accounting-based bank fundamentals to price global bank credit risk. First, we find that variables predicted by structural models are significantly associated with bank CDS spreads. Second, some CAMELS indicators contain incremental information for bank CDS prices. We find no evidence in favor of one model over the other, while the combined structural and CAMELS model performs better than each individual model. Moreover, leverage and asset quality have had a stronger impact on bank CDS since the onset of the recent financial crisis. Banks in countries with lower stock market volatility, fewer entry barriers, and/or more financial conglomerate restrictions tend to have lower credit risk. Deposit insurance appears to have an adverse effect on bank CDS spreads, indicating a moral hazard problem.

We show that time-varying volatility of volatility is a significant risk factor which affects the cross-section and the time-series of index and VIX option returns, beyond volatility risk itself. Volatility and volatility-of-volatility measures, identified model-free from the option price data as the VIX and VVIX indices, respectively, are only weakly related to each other. Delta-hedged index and VIX option returns are negative on average, and are more negative for strategies which are more exposed to volatility and volatility-of-volatility risks. Volatility and volatility of volatility significantly and negatively predict future delta-hedged option payoffs. The evidence is consistent with a no-arbitrage model featuring time-varying market volatility and volatility-of-volatility factors, both of which have negative market price of risk.

We study how contingent capital affects banks' risk choices. When triggered in highly levered states, going-concern conversion reduces risk-taking incentives, unlike conversion at default by traditional bail-inable debt. Interestingly, contingent capital (CoCo) may be less risky than bail-inable debt as its lower priority is compensated by a lower induced risk. The main benecial effect on risk incentives comes from reduced leverage upon conversion, while any equity dilution has the opposite effect. This is in contrast to traditional convertible debt, since CoCo bondholders have a short option position. As a result, principal writedown CoCo debt is most desirable for risk preventive purposes, although the effect may be tempered by a higher yield. The risk reduction effect of CoCo debt depends critically on the informativeness of the trigger. As it should ensure deleveraging in all states with high risk incentives, it is always inferior to pure equity.

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.

In this paper, we empirically investigate the impact of the credit risk of Eurozone member countries on the stability of the Euro. In the absence of a common euro bond, euro-area credit risk is induced though the credit default swaps of the member countries. The stability of the euro is examined by decomposing dollar-euro exchange rate options into the moments of the risk-neutral distribution. We document that during the sovereign debt crisis changes in the creditworthiness of member countries have significant impact on the stability of the euro. In particular, an increase in member countries’ credit risk results in an increase of volatility of the dollar-euro exchange rate along with soaring tail risk induced through the risk-neutral kurtosis. We find that member countries’ credit risk is a major determinant of the euro crash risk as measured by the risk-neutral skewness. We propose a new indicator for currency stability by combining the risk-neutral moments into an aggregated risk measure and show that our results are robust to this change in measure. Noticeable is the fact that during the sovereign debt crisis, the creditworthiness of countries with vulnerable fiscal positions is the main risk-endangering factor of the euro-stability.

The paper proposes a general asymmetric multifactor Wishart stochastic volatility (AMWSV) di usion process which accommodates leverage, feedback e ects and mul- tifactor for the covariance process. The paper gives the closed-form solution for the conditional and unconditional Laplace transform of the AMWSV models. The paper also suggests estimating the AMWSV model by the generalized method of moments using information not only of stock prices but also of realized volatilities and co- volatilities. The empirical results for the bivariate data of the NASDAQ 100 and S&P 500 indices show that the general AMWSV model is preferred among several nested models.

This paper evaluates the impact of accounting and market-driven information on the prediction of bankruptcy for Greek firms using the discrete hazard approach. The findings show that a hazard model that incorporates three accounting ratio components of Z-score and three market-driven variables is the most appropriate model for the prediction of corporate financial distress in Greece. This model outperforms a univariate model that uses the expected default frequency (EDF) derived from the Merton distance to default model, a multivariate model that is exclusively based on accounting variables, a model that combines EDF and accounting variables and a multivariate model that uses only market-driven variables. In-sample forecast accuracy tests confirm the main results. The out-of-sample evidence also suggests that the model yields the highest predictive ability during financial crisis when using data prior to the financial crisis.

We study how the exposure of fundamental and financial traders affects the futures curve of WTI oil and the market integration between WTI and Brent measured by their price spread. To obtain a parsimonious representation of the futures curve, we decompose it into a level-, a slope- and a curvature factor in a first step. In a second step, we separately regress each extracted factor on measures of the market exposure of fundamental and financial traders revealingwhether and how the exposure of the two trader groups affects the different dimensions of the futures curve. Spanning from 2006 until 2012, our dataset covers sub-periods of a sharp WTI-price rise as well as a diverging Brent-WTI-spread. Our contribution is threefold: First, we suggest that it is important to distinguish between level and slope as we find that fundamental traders have a measurable impact on the level of the futures curve, but do not play much of arole for its slope or curvature, whereas the exposure of financial traders mainly influences the slope of the futures curve. Despite allegations to the contrary, we find no evidence of a systematic impact of non-fundamental traders on the level of the futures curve during the steep 2006-2008 oil price surge. Second, we suggest using relative short- and relative long positions for fundamentaland financial traders instead of the net position as the former reflect better the overall group exposure and yield more significant results. Third, we find that the exposure of financials is the key driver of the Brent-WTI spread. It confirms that financial rather than fundamental traders are responsible for integrating the two markets.

There is a sizeable literature that tests for weak-form efficiency in commodity and energy spot and future prices. While many studies now allow for multiple structural breaks to address the criticism that conventional unit root tests have low power to reject the unit root null in the presence of structural change, the extant literature overlooks the fact that conventional unit root tests are biased in the presence of conditional heteroscedasticity. We apply a recently developed generalized autoregressive conditional heteroscedasticity (GARCH) unit root test with multiple structural breaks to crude palm oil spot and future prices and find much more evidence against weak-form efficiency than that found using tests that fail to allow for conditional heteroscedasticity. Our results point to the importance of allowing for heteroscedasticity when testing for efficiency in commodity and energy spot and future prices.

Financialization of commodity markets has been a broadly discussed topic in recent years. However, its implications for commodity investors have not yet been fully explored. This paper concentrates on the macroeconomic determinants of commodity returns in financialized and non-financialized markets and on their role for a tactical asset allocation. The study aims to contribute to the academic literature in four ways. First, it provides fresh evidence on the interdependences between commodity returns, inflation and the business activity. Second, it documents increased correlation of the commodity returns with the business activity in the financialized markets. Third, it explores changes in the lead/lag relationship of commodity prices and the business cycle. Fourth, it proves that the commodities retained their inflation hedging abilities in the financialized markets. The computations are based on listings of various commodity indices, which are calculated by S&P-GSCI, JP Morgan, and Dow Jones-UBS, between 1970 and 2013.

The Sharpe Ratio offers an excellent summary of the excess return required per unit of risk invested. This work presents an adaptation of the ex-ante Sharpe Ratio for currencies where we consider a random walk approach for the currency behavior and implied volatility as a proxy for market expectations of future realized volatility. The outcome of the proposed measure seems to gauge some information on the expected required return attached to the “peso problem”.

We suggest a joint analysis of ex-post intra-day variability in an option and its associated underlying asset market as a novel means of validating an option pricing model. For this purpose, we introduce the notion of option realized variance, by which we mean the cumulative variance realized by the sample path of successive option price observations. In concurrently observing the realized path of the underlying asset, we contrast option realized variance with the realized variance that would be implied from the underlying asset price path under certain model assumptions. In the empirical analysis, we focus on the implied volatility compensated Black-Scholes model and the Heston model. We find that neither model reconciles second-order moments in the option and the underlying asset market. The differences point to the existence of additional relevant pricing factors that affect option second-order moments. We thus corroborate findings made in option data of lower frequency.

This paper studies the behavior of the smile in the Warsaw Stock Exchange (WSE) during the volatile summer of 2011.We investigate the volatility smile derived from liquid call and put options on the Polish WIG20 index which option series expired on September 2011. In this period, the polish index has dropped about 20% in two weeks time. By linear interpolation, implied volatilities for moneyness points needed were calculated, then we construct 355 smile curves for calls and puts options to study and make some kind of smile-types classification. We propose seventeen types-smiles which represent all possible cases of three points (three moneynesses) graphical patterns. This classification is made basing upon relationship higher/equal/lower values of implied volatility for each of three points. Furthermore, we distinguish the convexity of pattern. We can note that smiles, smirks and ups are convex in shape, while reversed ones and downs are concave functions. / Este artículo analiza el comportamiento de la sonrisa de la volatilidad en la Bolsa de Varsovia (WSE) durante el volátil verano de 2011, derivada de las opciones más líquidas sobre el índice polaco WIG20, cuyas series expiraron en septiembre de 2011. En ese período, el índice había caído aproximadamente un 20% en tan sólo dos semanas. Mediante interpolación lineal construimos 355 curvas de sonrisas para poder estudiar una posible clasificación o tipología de las mismas. Proponemos 17 tipos de sonrisas, las cuales representan todos los casos posibles de tres monetizaciones de patrones gráficos. Esta clasificación se basa en la relación valores superiores/iguales/inferiores de la volatilidad implícita para cada uno de los tres puntos. Además, se distingue la convexidad de cada una. Destacamos que las sonrisas completas, las sonrisas asimétricas y las inclinadas hacia arriba son de forma convexa, mientras que las invertidas y las inclinadas hacia abajo son funciones cóncavas.

A contingent claims model of the value of sovereign guarantees of bank debt shows that the value decreases with the bank’s own creditworthiness and it increases with that of the sovereign as well as with bank and sovereign size. Using cross-sectional data for 188 large banks world-wide from 2007 to 2013, empirical results are consistent with the model’s implications, suggesting that the implicit support for a bank is higher when the bank is larger, when the bank is weaker, and when the country in which the bank is headquartered is larger or more creditworthy. While bank-specific factors matter as well as those related to the sovereign of the country where the bank is located, the effect on the value of sovereign guarantees of changes in bank strength dominate those in sovereign strength.

En este trabajo se generan estrategias especulativas en volatilidad con opciones europeas sobre veintiún componentes del Índice de Precios y Cotizaciones (IPC) y sobre este mismo índice, bajo el supuesto de que la volatilidad del activo subyacente es conducida por un proceso GARCH-M (1,1) calibrado con datos históricos, el precio de la opción se obtiene por simulación Monte Carlo. Con las estrategias de volatilidad construidas con los precios de las opciones simuladas se determinó que la estrategia cono corto es adecuada al realizar operaciones en el mercado mexicano (IPC) para plazos de 45 y 60 días, pero no para aquellas a 90 días. Asimismo se observó una relación directa entre el plazo y la estrategia cono corto en cuanto a ganancias al emplear ésta en el componente KIMBERA en el corto plazo, es decir, al aumentar el plazo, se obtuvo una mayor ganancia generada por dicha estrategia / In this paper speculative volatility strategies are generated with European options on twenty one components of the Mexican Stock Exchange (IPC) and on this same index, under the assumption that the volatility of the underlying asset is driven by a GARCH-M (1,1) process calibrated with historical data, the option price is obtained by Monte Carlo simulation. With volatility strategies built with simulated option prices, it was determined that the short cone strategy is adequate to trade in the Mexican market (IPC) for periods of 45 to 60 days, but not for 90 days periods. Moreover, a direct relationship between the term and the short cone strategy in terms of profits was observed when using this strategy in the KIMBERA component in the short term, i.e. by increasing the length a higher profit generated by such a strategy was obtained

This paper examined the petroleum futures volatility comovements and spillovers for crude oil, gasoline, heat oil and natural gas. The results of volatility analysis were used to calculate the optimal two-petroleum portfolio weights and hedging ratios. The data used in this study was the daily data from 2009 to 2014. The three Multivariate GARCH models, namely the VAR (1)-diagonal VECH, the VAR (1)-diagonal BEKK and the VAR (1)-CCC, were employed. The empirical results overall showed that the estimates of the multivariate GARCH parameters were statistically significant in almost all cases except in the case of RGASOLINE with RNG. This indicates that the short run persistence of shocks on the dynamic conditional correlations was greatest for RCRUDE with RHEATOIL, while the largest long run persistence of shocks to the conditional correlations for RCRUDE with RGASOLINE. Finally, the results from these optimal portfolio weights base on the VAR (1)-diagonal VECH estimates suggested that investors should had more heat oil than crude oil and other petroleum in their portfolio to minimize risk without lowering the expected return.

This paper empirically examines the relationship between trading volume and conditional volatility of returns in the Tunisian stock market within the framework of the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH). Through this study, we especially aim to test the volatility persistence degree without volume, with contemporaneous volume, and with lagged volume. Our empirical analysis is based on daily data related to the 43 most active and dynamic securities traded from January 2, 2008 to June 29, 2012. Our daily analysis reveals several results. Firstly, we confirm the strong positive relationship between trading volume and returns conditional volatility issued from generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) model. Secondly, according to the theoretical predictions of the MDH, we show that including contemporaneous trading volume in the conditional variance equation significantly reduces volatility persistence. Thirdly, through the addition of the lagged volume in the conditional variance equation, we show that volatility persistence remains in the whole at a high level and close to that obtained from the GARCH (1,1) model without trading volume, and also at a higher level than that resulting from the addition of the contemporaneous volume. Our results thus do not support the implications of the SIAH.

This review provides formal definitions of the terms credit value adjustment (CVA) and debt value adjustment (DVA). Estimating these quantities requires modeling the probabilities of default and the loss given default, recognizing the dependence structure among all these inputs. In practice, marginal distributions are used and a copula function assumed. Although it has long been known that different copula functions can produce very different price estimates, keeping marginal distributions constant, there is little empirical evidence about the appropriate form of function to use for modeling default dependence. This review discusses the use of collateral for risk mitigation and its effects on CVA. Regulators have argued that standardized contracts should be cleared through central counterparties (CCPs). However, there are arguments against CCPs.

This paper investigates the effects of financial institutions issuing contingent capital, a debt security that automatically converts into equity if assets fall below a predetermined threshold. We decompose bank liabilities into sets of barrier options and present closed-form solutions for their prices. We quantify the reduction in default probability associated with issuing contingent capital instead of subordinated debt. We then show that appropriate choice of contingent capital terms (in particular the conversion ratio) can virtually eliminate stockholders' incentives to risk-shift, a motivation that is present when bank liabilities instead include either subordinated debt or additional equity. Importantly, risk-taking incentives continue to be weak during times of financial distress. Our findings imply that contingent capital may be an effective tool for stabilizing financial institutions.

This paper investigates a duopoly with two alternative investment projects. We examine a situation in which a firm cannot invest in any project that has been taken by the rival firm. The first mover's advantage in project choice leads to an equilibrium quite different from that in previous studies. Specifically, we show that in equilibrium, the investment time and the option value are between those in a duopoly with one project and a monopoly with one project. Moreover, we show that a high correlation between project values, unlike in a monopoly with two projects, plays a positive role in mitigating preemptive competition. The results complement the literature of real options games and of max-options and entail new empirical implications.

Empirical studies on credit spread determinants are predicated on the presence of a single-regime over the entire sample period and thus find limited explanatory power. We show that a single regime model hides the fact that the explanatory variables take on different loadings across changing patterns in credit spreads. We capture these hidden effects by modeling endogenous (rating-specific) regimes for credit spreads. We find that in a two regime-based model traditional determinants have significant explanatory power consistent with the prediction of structural models, yet their importance changes across regimes -- some variables have their effects strengthen, weaken or even reverse signs across regimes. We also investigate the differing behavior of these loadings across different specifications of the economic cycle and find that endogenous regimes best capture the hidden effects of these variables with the highest explanatory power for the same set of variables.

Market impact is one of the most important aspects of real-world trading. It is typically a major component of transaction costs, and trading strategies are carefully optimized to minimize it. But most of the research has focused on one-off trades, not the repeated series of transactions required in delta-hedging an options position. In this article, the authors explore optimal hedging in an illiquid environment for a large trader whose trades absorb a nonmarginal fraction of the standing orders in the limit order book. The trader needs to take account of both the immediate price impact of each trade and the speed at which the limit order book is restored afterward—the market’s resilience. These two aspects of market dynamics together can produce a permanent effect of a big trade and also a temporary one that dissipates over time. Depending on the parameters of the liquidity process, the optimal strategy can be the canonical Black–Scholes delta hedge (in the limiting case of infinite liquidity) or one that is distinctly less volatile (when market impact is large, but there is some resilience in prices) or even one that entails price manipulation (when there is no resilience and the price impact is permanent).

This paper derives efficient pricing formulae for renewable energy Feed-in Tariff (FiT) designs that incorporate exposure to uncertain market prices by using option pricing theory. Such FiT designs are presented as a means to delineate market price risk amongst investors and policymakers when designing renewable energy support schemes. Sequential game theory provides the theoretical framework through which we model the strategic interaction of policymakers and investors during policy formulation. This model is solved using option pricing theory when a FiT is comprised of market prices combined with a guaranteed element. This solution also allows for an analytical formulation of the policy cost of subsidisation. Partial derivatives characterise sensitivity of policy cost and investor remuneration to deviations in market conditions beyond those expected. Analytical derivations provide a set of tools which may guide more efficient FiT policy and investment decisions. Numerical simulations demonstrate application for a stylised Irish case study, with a scenario analysis providing further insight into the relative sensitivity of policy cost and investor remuneration under different market conditions.

Fluctuations in commodity prices are a major concern to many market participants. This paper uses realized volatility methods to calculate daily volatility and correlation estimates for three grain futures prices (corn, soybean and wheat). The realized volatility estimates exhibit the properties consistent with the stylized facts observed in earlier studies. According to daily realized correlations and regression coefficients, the spot returns from the three grain futures are positively related. The realized estimates are then used to evaluate the degree of volatility transmissions across grain future prices. The impulse response analysis is conducted by fitting the vector autoregressive model to realized volatility and correlation estimates, using the bootstrap method for statistical inference. The results indicate that there exist rich dynamic interactions among the volatilities and correlations across the grain futures markets.

Volatility smiles arise in currency option markets when empirical exchange rate returns distributions exhibit leptokurtosis. This feature of empirical distributions is symptomatic of turbulent periods when exchange rate movements are in excess of movements based on the assumption of normality. In contrast, during periods of tranquility, movements in exchange rates are relatively small, resulting in unconditional empirical returns distributions with thinner tails than the normal distribution. Pricing currency options during tranquil periods on the assumption of normal returns yields implied volatility frowns, with over-pricing at both deep-in and deep-out-of-the-money contracts and under-pricing for at-the-money contracts. This paper shows how a parametric class of thin-tailed distributions based on the generalized Student t family of distributions can price currency options during periods of tranquility.

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained by applying the Kalman filter and smoother to a linearization of a state-space representation of the model. The method is illustrated using the Heston (1993) stochastic volatility model applied to Australian News Corporation spot and option price data. Alternative models nested in the Heston framework are ranked via Bayes Factors and via fit, predictive and hedging performance.

We investigate the informational content of options-implied probability density functions (PDFs) for the future price of oil. Using a semiparametric variant of the methodology in Breeden and Litzenberger (1978), we investigate the fit and smoothness of distributions derived from alternative PDF estimation methods, and develop a set of robust summary statistics. Using PDFs estimated around episodes of high geopolitical tensions, oil supply disruptions, macroeconomic data releases, and shifts in OPEC production strategy, we explore the extent to which oil price movements are expected or unexpected, and whether agents believe these movements to be persistent or temporary.

L’analyse systematiquement dualiste des bulles speculatives ne permet pas d’identifier les differents comportements des investisseurs selon qu’ils operent sur le marche des actifs sous-jacents ou sur le marche des produits derives. L’etude de la Bulle des Mers du Sud est, a cet egard, eclairante. Dans cet article, nous montrons qu’en depit d’une evaluation rationnelle des produits derives financiers de la Compagnie des Mers du Sud (les titres de souscription) en tant qu’options d’achat, l’evaluation de l’actif sous-jacent (l’action cms) a souffert d’une confusion liee aux revenus futurs de l’activite de restructuration de la dette publique.

We develop a dynamic model in which a firm exercises an option to expand production with a cash balance and costly external funds. By proving the properties of the option exercise regions, we reveal how the firm's financing and investment policy depends on its cash flow and balance. In the presence of only a proportional cost of external financing, the firm with more cash balance invests earlier; however, the presence of both proportional and fixed costs leads to a non-monotonic relation between the investment time and the cash balance. The firm with more cash balance invests later to save a fixed cost, particularly when the cash balance is close to the investment cost. Our results can potentially account for a variety of empirical results concerning the relation between investment and internal funds.

This study proposes models that can be used as shorthand analysis tools for CDS spreads and CDS spread changes. For this purpose we examine the determinants of CDS spreads and spread changes on a broad database of 718 US firms during the period from early 2002 to early 2013. Contrary to previous studies, we discover that market variables still have explanatory power after controlling for firm-specific variables inspired by structural models. Three explanatory variables appear to overshadow the other variables examined in this paper: Stock Return, ?Volatility (the change in stock return volatility) and ?MRI (change in the median CDS spread in the rating class). We also discover that models used in the event study literature to explain spread changes can be improved by using additional market variables. Further, we show that ratings explain cross-section variation in CDS spreads even after controlling for structural model variables.

Confidence intervals and joint confidence sets are constructed for the nonparametric calibration of exponential L\'evy models based on prices of European options. To this end, we show joint asymptotic normality in the spectral calibration method for the estimators of the volatility, the drift, the jump intensity and the L\'evy density at finitely many points., Comment: to appear in Finance and Stochastics

Intraday return volatility is characterized by the contemporaneous presence of periodicity and long memory. This article proposes two new parameterizations of the intraday volatility process that account for both features: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH. The analysis of hourly E-mini S&P 500 futures returns shows that the volatility is characterized by a statistically significant long-range dependence coupled with a periodic leverage effect, with negative return shocks having a larger effect on volatility during the US trading period. Long memory estimates obtained with nonperiodic long memory models are greater than those obtained with FI-PEGARCH and SFI-PEGARCH models. A simulation experiment shows that the long memory component can be strongly biased when periodic patterns are not properly modelled at the intraday level. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts.