Ryszard Kokoszczyński

Structural Econometric Models in Forecasting Inflation at the National Bank of Poland

The paper presents the procedure and two structural macroeconometric models used at the National Bank of Poland for producing regular quarterly inflation projections. One of the models is a small macroeconomic model based on the New Keynesian Phillips curve, the IS curve and the exchange rate equation based on uncovered interest parity with risk factors. The other, more disaggregated model, explicitly focuses on the supply side and separates the steady state from short-term adjustments.

Poland Before the Euro

The euro is relevant for decisions that the Polish Central Bank takes about its general rules of monetary policy, goals and decision structures, but it is not, or at least not yet, relevant to the everyday processes of controlling money supply and interest rates. Fighting inflation was a primary target of the central bank prior to applying for entry to the European Union and maintaining a satisfactory external account. However, with the growing openness of the economy, eclectic monetary policy showed a high degree of internal inconsistency. With greater autonomy under a new constitution, the Central Bank adopted direct inflation targeting. The adoption of this measure is consistent with emerging EU policy but was driven by specifically Polish concerns.

Financial Inflows to Poland, 1990–96

Foreign exchange flows in Poland in the 1990s, especially during 1994–96, resulted from two developments. First, there was a current account surplus, as growth revived due to efficiency improvements, while macroeconomic policy limited domestic demand. Second, Poland had rejoined international capital markets and regained favorable credit ratings, triggering investment inflows. We can classify the effects of these inflows into three groups: changes in the institutional framework; changes in the stabilization path; and changes in the real economy. The crawling band introduced in May 1995, rapid development of the money market, and improvement of the central banks capacity to intervene on that market are in the first group. As to the second, relative currency appreciation and import competition, constraining domestic price increases, contributed to the strong disinflationary push which began in 1995. Under the third heading, foreign direct investment and portfolio inflows helped to maintain rapid growth of investment and output.

Option Pricing Models with HF Data – a Comparative Study. The Properties of Black Model with Different Volatility Measures

This paper compares option pricing models, based on Black model notion (Black, 1976), especially focusing on the volatility models implied in the process of pricing. We calculated the Black model with historical (BHV), implied (BIV) and several different types of realized (BRV) volatility (additionally searching for the optimal interval Δ, and parameter n - the memory of the process). Our main intention was to find the best model, i.e. which predicts the actual market price with minimum error. We focused on the HF data and bidask quotes (instead of transactional data) in order to omit the problem of non-synchronous trading and additionally to increase the significance of our research through numerous observations. After calculation of several error statistics (RMSE, HMAE and HRMSE) and additionally the percent of price overpredictions, the results confirmed our initial intuition that that BIV is the best model, BHV being the second best, and BRV – the least efficient of them. The division of our database into different classes of moneyness ratio and TTM enabled us to observe the distinct differences between compared pricing models. Additionally, focusing on the same pricing model with different volatility processes results in the conclusion that point-estimate, not averaged process of RV is the main reason of high errors and instability of valuation in high volatility environment. Finally, we have been able to detect “spurious outliers” and explain their effect and the reason for them owing to the multi-dimensional comparison of the pricing error statistics.

Midquotes or Transactional Data? The Comparison of Black Model on HF Data

The main idea of this research is to check the efficiency of the Black option pricing model on the basis of HF emerging market data. However, liquidity constraints - a typical feature of an emerging derivatives market - put severe limits for conducting such a study. That is the reason why Kokoszczynski et al., 2010, have conducted their earlier research on midquotes data treating them as potential transactional data. They have got some intriguing conclusions about implementing different volatility processes into the Black option model. Nevertheless, taking into account that midquotes do not have to be the proper representation of market prices as probably transactional data do, we decide to compare in this paper the results of the research conducted on HF transactional and midquotes data. This comparison shows that the results do not differ significantly between these two approaches and that BIV model significantly outperforms other models, especially BRV model with the latter producing the worst results. Additionally, we provide the discussion of liquidity issue in the context of emerging derivatives market. Finally, after exclusion of spurious outliers we observe significant patterns in option pricing that are not visible on the raw data.

Volatility as a New Class of Assets? The Advantages of Using Volatility Index Futures in Investment Strategies

This paper investigates the changes in the investment portfolio performance after including VIX. We apply different models for optimal portfolio selection (Markowitz and Black-Litterman) assuming both the possibility of short sale and the lack of it. We also use various assets, data frequencies, and investment horizons to get a comprehensive picture of our results’ robustness. Investment strategies including VIX futures do not always deliver higher returns or higher Sharpe ratios for the period 2006-2013. Their performance is quite sensitive to changes in model parameters. However, including VIX significantly increases the returns in almost all cases during the recent financial crisis. This result clearly emphasizes potential gains of having such an asset in the portfolio in case of very high volatility in financial markets.

Does Historical Volatility Term Structure Contain Valuable Information for Predicting Volatility and Index Futures

We suggest that the term structure of volatility futures (e.g. VIX futures) shows a clear pattern of dependence on the current level of VIX index. At the low level of VIX (below 20) the term structure is highly upward sloping; at the high VIX level (over 30) it is strongly downward sloping. We use those features to better predict future volatility and index futures. We begin by introducing some quantitative measures of volatility term structure (VTS) and volatility risk premium (VRP). We use them further to estimate the distance between the actual value and the fair (model) value of the VTS. We find that this distance has significant predictive power for volatility futures and index futures and we use this feature to design a simple strategy to invest in VIX index futures and S&P500.

Options Delta Hedging with No Options at All

The adjustment speed of delta hedged options exposure depends on the market realized and implied volatility. We observe that by consistently hedging long and short positions in options we can eventually end up with pure exposure to volatility without any options in the portfolio at all. The results of such arbitrage strategy is based only on speed of adjustment of delta hedged option positions. More specifically, they rely on interrelation between realized volatility levels calculated for various time intervals (from daily to intraday frequency). Theoretical intuition enables us to solve the puzzle of the optimal frequency of hedge adjustment and its influence on hedging efficiency. We present results of a simple hedge strategy based on the consistent hedging of a portfolio of options for various worldwide equity indice

Which Option Pricing Model is the Best? High Frequency Data for Nikkei225 Index Options

Option pricing models are the main subject of many research papers prepared both in academia and financial industry. Using high-frequency data for Nikkei225 index options, we check the properties of option pricing models with different assumptions concerning the volatility process (historical, realized, implied, stochastic or based on GARCH model). In order to relax the continuous dividend payout assumption, we use the Black model for pricing options on futures, instead of the Black-Scholes-Merton model. The results are presented separately for 5 classes of moneyness ratio and 5 classes of time to maturity in order to show some patterns in option pricing and to check the robustness of our results. The Black model with implied volatility (BIV) comes out as the best one. Highest average pricing errors we obtain for the Black model with realized volatility (BRV). As a result, we do not see any additional gain from using more complex and time-consuming models (SV and GARCH models. Additionally, we describe liquidity of the Nikkei225 option pricing market and try to compare our results with a detailed study for the emerging market of WIG20 index options (Kokoszczynski et al. 2010b).